From ad1ddbfe9019c9069b35eba84cc7e447468dd065 Mon Sep 17 00:00:00 2001 From: vicpaton Date: Tue, 10 Sep 2024 14:53:50 +0200 Subject: [PATCH] updated vignettes --- docs/src/vignettes/1_quickstart.ipynb | 12 +- docs/src/vignettes/2_multiple_methods.ipynb | 732 +++++++++--------- .../vignettes/3_evaluation_offt_path.ipynb | 194 ++--- docs/src/vignettes/4_moon.ipynb | 26 +- .../src/vignettes/5_evaluation_decryptm.ipynb | 65 +- 5 files changed, 517 insertions(+), 512 deletions(-) diff --git a/docs/src/vignettes/1_quickstart.ipynb b/docs/src/vignettes/1_quickstart.ipynb index 324e958..6f33f62 100644 --- a/docs/src/vignettes/1_quickstart.ipynb +++ b/docs/src/vignettes/1_quickstart.ipynb @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -278,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -747,10 +747,10 @@ "\n" ], "text/plain": [ - ">" + ">" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/src/vignettes/2_multiple_methods.ipynb b/docs/src/vignettes/2_multiple_methods.ipynb index 5423152..32d166f 100644 --- a/docs/src/vignettes/2_multiple_methods.ipynb +++ b/docs/src/vignettes/2_multiple_methods.ipynb @@ -628,7 +628,7 @@ "\n" ], "text/plain": [ - ">" + ">" ] }, "execution_count": 7, @@ -955,7 +955,7 @@ "\n" ], "text/plain": [ - ">" + ">" ] }, "execution_count": 9, @@ -998,407 +998,429 @@ "\n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "EGFR\n", - "\n", + "\n", "\n", "\n", "\n", "MAP2K1\n", - "\n", + "\n", "\n", "\n", "\n", "EGFR->MAP2K1\n", - "\n", - "\n", + "\n", + "\n", "\n", - 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">" + ">" ] }, "execution_count": 18, @@ -3104,7 +3126,7 @@ "\n" ], "text/plain": [ - ">" + ">" ] }, "execution_count": 20, @@ -3153,33 +3175,39 @@ "name": "stdout", "output_type": "stream", "text": [ - "(CORNETO) Sep 10 01:10:52 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 10 01:10:52 PM - INFO : 19/25 outputs mapped to the graph\n", - "(CORNETO) Sep 10 01:10:52 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 10 01:10:52 PM - INFO : Finished. Final size: V x E = (1186, 5782).\n", - "(CORNETO) Sep 10 01:10:52 PM - INFO : 1/1 inputs after pruning.\n", - "(CORNETO) Sep 10 01:10:52 PM - INFO : 12/19 outputs after pruning.\n", - "(CORNETO) Sep 10 01:10:52 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 10 01:10:53 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 10 01:10:53 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 10 01:10:53 PM: Your problem has 26857 variables, 65384 constraints, and 0 parameters.\n", - "(CVXPY) Sep 10 01:10:53 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 10 01:10:53 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 10 01:10:53 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 10 01:10:53 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 10 01:10:53 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 10 01:10:53 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 10 01:10:53 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 10 01:10:53 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 10 01:10:53 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 10 01:10:53 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 10 01:10:53 PM: Finished problem compilation (took 1.380e-01 seconds).\n", - "(CVXPY) Sep 10 01:10:53 PM: Invoking solver GUROBI to obtain a solution.\n", - "(CVXPY) Sep 10 01:10:57 PM: Problem status: optimal\n", - "(CVXPY) Sep 10 01:10:57 PM: Optimal value: 1.328e+01\n", - "(CVXPY) Sep 10 01:10:57 PM: Compilation took 1.380e-01 seconds\n", - "(CVXPY) Sep 10 01:10:57 PM: Solver (including time spent in interface) took 4.141e+00 seconds\n", - "(CORNETO) Sep 10 01:10:57 PM - INFO : Finished in 5.72 s.\n" + "(CORNETO) Sep 10 01:54:23 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 10 01:54:23 PM - INFO : 19/25 outputs mapped to the graph\n", + "(CORNETO) Sep 10 01:54:23 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(CORNETO) Sep 10 01:54:24 PM - INFO : Finished. Final size: V x E = (1186, 5782).\n", + "(CORNETO) Sep 10 01:54:24 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 10 01:54:24 PM - INFO : 12/19 outputs after pruning.\n", + "(CORNETO) Sep 10 01:54:24 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 10 01:54:25 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 10 01:54:25 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 10 01:54:25 PM: Your problem has 26857 variables, 65384 constraints, and 0 parameters.\n", + "(CVXPY) Sep 10 01:54:25 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 10 01:54:25 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 10 01:54:25 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 10 01:54:25 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 10 01:54:25 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 10 01:54:25 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 10 01:54:25 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 10 01:54:25 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 10 01:54:25 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 10 01:54:25 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 10 01:54:25 PM: Finished problem compilation (took 2.218e-01 seconds).\n", + "(CVXPY) Sep 10 01:54:25 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 10 01:54:33 PM: Problem status: optimal\n", + "(CVXPY) Sep 10 01:54:33 PM: Optimal value: 1.328e+01\n", + "(CVXPY) Sep 10 01:54:33 PM: Compilation took 2.218e-01 seconds\n", + "(CVXPY) Sep 10 01:54:33 PM: Solver (including time spent in interface) took 8.116e+00 seconds\n", + "(CORNETO) Sep 10 01:54:33 PM - INFO : Finished in 10.04 s.\n" ] } ], @@ -3208,313 +3236,325 @@ "\n", "\n", "AKT1\n", - "\n", - "AKT1\n", + "\n", + "AKT1\n", "\n", "\n", "\n", "SMARCC1\n", - "\n", + "\n", "\n", "\n", "\n", "AKT1->SMARCC1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "GSK3B\n", - "\n", - "GSK3B\n", + "\n", + "GSK3B\n", "\n", "\n", "\n", "AKT1->GSK3B\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "PHF20\n", - "\n", + "\n", "\n", "\n", "\n", "AKT1->PHF20\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "NFKB1\n", - "\n", + "\n", "\n", "\n", "\n", "GSK3B->NFKB1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "PHLPP1\n", - "\n", - "PHLPP1\n", + "\n", + "PHLPP1\n", "\n", "\n", "\n", "GSK3B->PHLPP1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "SFPQ\n", - "\n", + "\n", "\n", "\n", "\n", "GSK3B->SFPQ\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "PRKCA\n", - "\n", - "PRKCA\n", + "\n", + "PRKCA\n", "\n", "\n", "\n", "PHLPP1->PRKCA\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "NR1H4\n", - 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"TP53\n", + "\n", + "TP53\n", "\n", "\n", - "\n", + "\n", "TP53->CASP6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "HIC1\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "TP53->HIC1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "PRKDC\n", - "\n", - "PRKDC\n", + "\n", + "PRKDC\n", "\n", "\n", - "\n", + "\n", "PRKDC->AKT1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "PRKDC->TP53\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "EGFR\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "EGFR->PRKDC\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "E2F1\n", - "\n", - "E2F1\n", + "\n", + "E2F1\n", "\n", "\n", - "\n", + "\n", "EGFR->E2F1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - ">" + ">" ] }, "execution_count": 22, diff --git a/docs/src/vignettes/3_evaluation_offt_path.ipynb b/docs/src/vignettes/3_evaluation_offt_path.ipynb index 6a42c5c..4dc8066 100644 --- a/docs/src/vignettes/3_evaluation_offt_path.ipynb +++ b/docs/src/vignettes/3_evaluation_offt_path.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -99,135 +99,40 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(CORNETO) Sep 06 02:42:06 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 06 02:42:06 PM - INFO : 17/25 outputs mapped to the graph\n", - "(CORNETO) Sep 06 02:42:06 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 06 02:42:06 PM - INFO : Finished. Final size: V x E = (1188, 5786).\n", - "(CORNETO) Sep 06 02:42:06 PM - INFO : 1/1 inputs after pruning.\n", - "(CORNETO) Sep 06 02:42:06 PM - INFO : 12/17 outputs after pruning.\n", - "(CORNETO) Sep 06 02:42:06 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 06 02:42:06 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 06 02:42:07 PM - INFO : Preprocess completed.\n", - "===============================================================================\n", - " CVXPY \n", - " v1.5.2 \n", - "===============================================================================\n", - "(CVXPY) Sep 06 02:42:07 PM: Your problem has 26879 variables, 65436 constraints, and 0 parameters.\n", - "(CVXPY) Sep 06 02:42:07 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 06 02:42:07 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 06 02:42:07 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 06 02:42:07 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "-------------------------------------------------------------------------------\n", - " Compilation \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Sep 06 02:42:07 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 06 02:42:07 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 06 02:42:07 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 06 02:42:07 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 06 02:42:07 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 06 02:42:07 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 06 02:42:07 PM: Finished problem compilation (took 4.024e-01 seconds).\n", - "-------------------------------------------------------------------------------\n", - " Numerical solver \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Sep 06 02:42:07 PM: Invoking solver GUROBI to obtain a solution.\n", - "Set parameter Username\n", - "Academic license - for non-commercial use only - expires 2025-09-06\n", - "Set parameter QCPDual to value 1\n", - "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (linux64 - \"Ubuntu 22.04.1 LTS\")\n", - "\n", - "CPU model: 11th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80GHz, instruction set [SSE2|AVX|AVX2|AVX512]\n", - "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n", - "\n", - "Optimize a model with 65436 rows, 26879 columns and 152735 nonzeros\n", - "Model fingerprint: 0xf5d2afc2\n", - "Variable types: 7021 continuous, 19858 integer (19858 binary)\n", - "Coefficient statistics:\n", - " Matrix range [1e-03, 1e+03]\n", - " Objective range [1e-02, 1e+00]\n", - " Bounds range [1e+00, 1e+00]\n", - " RHS range [1e-03, 1e+03]\n", - "Presolve removed 29135 rows and 2369 columns\n", - "Presolve time: 0.34s\n", - "Presolved: 36301 rows, 24510 columns, 108489 nonzeros\n", - "Variable types: 6590 continuous, 17920 integer (17920 binary)\n", - "Deterministic concurrent LP optimizer: primal and dual simplex\n", - "Showing primal log only...\n", - "\n", - "Concurrent spin time: 0.02s\n", - "\n", - "Solved with dual simplex\n", - "Extra simplex iterations after uncrush: 4\n", - "\n", - "Root relaxation: objective -1.266381e+01, 2081 iterations, 0.26 seconds (0.25 work units)\n", - "\n", - " Nodes | Current Node | Objective Bounds | Work\n", - " Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n", - "\n", - " 0 0 -12.66381 0 103 - -12.66381 - - 1s\n", - "H 0 0 -0.9700000 -12.66381 1206% - 1s\n", - "H 0 0 -1.8700000 -12.66381 577% - 1s\n", - "H 0 0 -1.9100000 -12.66381 563% - 1s\n", - " 0 0 -12.65750 0 135 -1.91000 -12.65750 563% - 1s\n", - " 0 0 -12.65000 0 174 -1.91000 -12.65000 562% - 2s\n", - " 0 0 -12.65000 0 172 -1.91000 -12.65000 562% - 2s\n", - " 0 0 -12.65000 0 206 -1.91000 -12.65000 562% - 3s\n", - " 0 0 -12.65000 0 34 -1.91000 -12.65000 562% - 5s\n", - " 0 0 -12.65000 0 34 -1.91000 -12.65000 562% - 6s\n", - "H 0 0 -12.6200000 -12.65000 0.24% - 8s\n", - " 0 0 -12.65000 0 46 -12.62000 -12.65000 0.24% - 8s\n", - " 0 0 -12.65000 0 46 -12.62000 -12.65000 0.24% - 9s\n", - " 0 0 -12.65000 0 52 -12.62000 -12.65000 0.24% - 9s\n", - " 0 0 -12.65000 0 49 -12.62000 -12.65000 0.24% - 10s\n", - " 0 0 -12.65000 0 90 -12.62000 -12.65000 0.24% - 11s\n", - " 0 0 -12.65000 0 90 -12.62000 -12.65000 0.24% - 11s\n", - " 0 0 -12.65000 0 26 -12.62000 -12.65000 0.24% - 13s\n", - " 0 0 -12.65000 0 25 -12.62000 -12.65000 0.24% - 13s\n", - " 0 0 -12.65000 0 19 -12.62000 -12.65000 0.24% - 14s\n", - "H 0 0 -12.6300000 -12.65000 0.16% - 14s\n", - " 0 0 -12.65000 0 19 -12.63000 -12.65000 0.16% - 15s\n", - " 0 0 -12.65000 0 19 -12.63000 -12.65000 0.16% - 15s\n", - " 0 2 -12.65000 0 19 -12.63000 -12.65000 0.16% - 17s\n", - " 40 20 -12.65000 11 52 -12.63000 -12.65000 0.16% 184 20s\n", - " 212 76 -12.64999 28 27 -12.63000 -12.65000 0.16% 62.5 25s\n", - " 421 116 -12.65000 5 157 -12.63000 -12.65000 0.16% 45.5 30s\n", - " 543 158 -12.65000 15 69 -12.63000 -12.65000 0.16% 56.1 35s\n", - " 783 241 -12.64996 34 65 -12.63000 -12.65000 0.16% 50.5 41s\n", - "H 785 219 -12.6400000 -12.65000 0.08% 50.5 41s\n", - " 801 36 -12.65000 17 10 -12.64000 -12.65000 0.08% 49.9 45s\n", - "\n", - "Cutting planes:\n", - " Gomory: 9\n", - " Implied bound: 13\n", - " Clique: 2\n", - " MIR: 6\n", - " Flow cover: 12\n", - " Zero half: 18\n", - " RLT: 2\n", - "\n", - "Explored 806 nodes (103630 simplex iterations) in 47.95 seconds (37.06 work units)\n", - "Thread count was 8 (of 8 available processors)\n", - "\n", - "Solution count 8: -12.64 -12.64 -12.63 ... -0.97\n", - "No other solutions better than -12.64\n", - "\n", - "Optimal solution found (tolerance 1.00e-04)\n", - "Best objective -1.264000000000e+01, best bound -1.264000000000e+01, gap 0.0000%\n", - "-------------------------------------------------------------------------------\n", - " Summary \n", - "-------------------------------------------------------------------------------\n", - "(CVXPY) Sep 06 02:42:55 PM: Problem status: optimal\n", - "(CVXPY) Sep 06 02:42:55 PM: Optimal value: 1.336e+01\n", - "(CVXPY) Sep 06 02:42:55 PM: Compilation took 4.024e-01 seconds\n", - "(CVXPY) Sep 06 02:42:55 PM: Solver (including time spent in interface) took 4.824e+01 seconds\n", - "(CORNETO) Sep 06 02:42:55 PM - INFO : Finished in 49.56 s.\n" + "(CORNETO) Sep 10 01:59:50 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 10 01:59:50 PM - INFO : 17/25 outputs mapped to the graph\n", + "(CORNETO) Sep 10 01:59:50 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 10 01:59:50 PM - INFO : Finished. Final size: V x E = (1188, 5786).\n", + "(CORNETO) Sep 10 01:59:50 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 10 01:59:50 PM - INFO : 12/17 outputs after pruning.\n", + "(CORNETO) Sep 10 01:59:50 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 10 01:59:51 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 10 01:59:51 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 10 01:59:51 PM: Your problem has 26879 variables, 65436 constraints, and 0 parameters.\n", + "(CVXPY) Sep 10 01:59:51 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 10 01:59:51 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 10 01:59:51 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 10 01:59:51 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 10 01:59:51 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 10 01:59:51 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 10 01:59:51 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 10 01:59:51 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 10 01:59:51 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 10 01:59:51 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 10 01:59:51 PM: Finished problem compilation (took 1.544e-01 seconds).\n", + "(CVXPY) Sep 10 01:59:51 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 10 02:00:04 PM: Problem status: optimal\n", + "(CVXPY) Sep 10 02:00:04 PM: Optimal value: 1.336e+01\n", + "(CVXPY) Sep 10 02:00:04 PM: Compilation took 1.544e-01 seconds\n", + "(CVXPY) Sep 10 02:00:04 PM: Solver (including time spent in interface) took 1.267e+01 seconds\n", + "(CORNETO) Sep 10 02:00:04 PM - INFO : Finished in 14.11 s.\n" ] } ], @@ -259,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -298,9 +203,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_15858/126159575.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "/tmp/ipykernel_15858/126159575.py:4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" + ] + } + ], "source": [ "signatures = dc.get_resource('MSigDB', organism='human')\n", "biocarta_elements = signatures[signatures['collection'] == 'biocarta_pathways']\n", @@ -310,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -319,12 +239,12 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -338,7 +258,7 @@ "
" ] }, - "execution_count": 45, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -386,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -405,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -517,7 +437,7 @@ "6 corneto " ] }, - "execution_count": 43, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/src/vignettes/4_moon.ipynb b/docs/src/vignettes/4_moon.ipynb index ddd3fe4..042fcd9 100644 --- a/docs/src/vignettes/4_moon.ipynb +++ b/docs/src/vignettes/4_moon.ipynb @@ -254,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -272,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -281,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -297,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -317,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -333,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -363,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -372,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -381,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -412,7 +412,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -435,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -451,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -460,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ diff --git a/docs/src/vignettes/5_evaluation_decryptm.ipynb b/docs/src/vignettes/5_evaluation_decryptm.ipynb index 5eaa339..0072164 100644 --- a/docs/src/vignettes/5_evaluation_decryptm.ipynb +++ b/docs/src/vignettes/5_evaluation_decryptm.ipynb @@ -326,27 +326,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_6093/3695212997.py:1: SettingWithCopyWarning: \n", + "/tmp/ipykernel_16531/3695212997.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "/tmp/ipykernel_6093/3695212997.py:1: SettingWithCopyWarning: \n", + "/tmp/ipykernel_16531/3695212997.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "/tmp/ipykernel_6093/3695212997.py:1: SettingWithCopyWarning: \n", + "/tmp/ipykernel_16531/3695212997.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "/tmp/ipykernel_6093/3695212997.py:1: SettingWithCopyWarning: \n", + "/tmp/ipykernel_16531/3695212997.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "/tmp/ipykernel_6093/3695212997.py:1: SettingWithCopyWarning: \n", + "/tmp/ipykernel_16531/3695212997.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1020,11 +1020,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(CORNETO) Sep 10 02:03:51 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 10 02:03:51 PM - INFO : 21/30 outputs mapped to the graph\n", + "(CORNETO) Sep 10 02:03:51 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 10 02:03:51 PM - INFO : Finished. Final size: V x E = (1187, 5792).\n", + "(CORNETO) Sep 10 02:03:51 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 10 02:03:51 PM - INFO : 13/21 outputs after pruning.\n", + "(CORNETO) Sep 10 02:03:51 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 10 02:03:51 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 10 02:03:52 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 10 02:03:52 PM: Your problem has 26932 variables, 65577 constraints, and 0 parameters.\n", + "(CVXPY) Sep 10 02:03:52 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 10 02:03:52 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 10 02:03:52 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 10 02:03:52 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 10 02:03:52 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 10 02:03:52 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 10 02:03:52 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 10 02:03:52 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 10 02:03:52 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 10 02:03:52 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 10 02:03:52 PM: Finished problem compilation (took 1.270e-01 seconds).\n", + "(CVXPY) Sep 10 02:03:52 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 10 02:04:07 PM: Problem status: optimal\n", + "(CVXPY) Sep 10 02:04:07 PM: Optimal value: 9.363e+00\n", + "(CVXPY) Sep 10 02:04:07 PM: Compilation took 1.270e-01 seconds\n", + "(CVXPY) Sep 10 02:04:07 PM: Solver (including time spent in interface) took 1.493e+01 seconds\n", + "(CORNETO) Sep 10 02:04:07 PM - INFO : Finished in 16.04 s.\n" + ] + } + ], "source": [ - "network_dict['corneto_network'] = nc.methods.run_corneto_carnival(corneto_graph, source_dict, target_dict, betaWeight=0.01, solver='GUROBI')" + "network_dict['corneto_network'] = nc.methods.run_corneto_carnival(network, source_dict, target_dict, betaWeight=0.01, solver='GUROBI')" ] }, { @@ -1088,12 +1122,23 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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bt75hTfHx8Tpz5oyaNm2qP//803oK+Lp165Samqqnn37a5rODBw/O1N/SpUvVuHFjBQYG2tTVqlUrpaWlaePGjdcdn+x8N251vbPz9woICNDvv/+umJiY69aWG9ndL94KJycn9e7dWytXrrT5vi9cuFANGjRQ6dKlr/tZf39/SdLq1auzvExJklasWKH09HS99NJLcnKy/adbxnds7dq1Sk5O1jPPPGMzT//+/eXn55fpnjFZ7cezu8/N2Da++OKLbF0+AgC4MxD+gTuAn5+fpOyHxCNHjsjJyUlly5a1mR4aGqqAgAAdOXLEOq1Hjx46duyYtm7dKunKowR37dqlHj16WOeJiYmRMUblypVTSEiIzWv//v3Wm89lKFasmNzc3DLV9fvvv+uBBx6Qv7+//Pz8FBISYr0ZXkZwyajt2tpdXFwUHh5uMy0mJka///57pprKly8vSZnqulaPHj10+fJl6/XwFy5c0DfffGO9jl6SmjZtqq5du2rs2LEqVKiQOnXqpLlz5970ngLZkfGjQ3Z/1LmZgwcPyhij0aNHZxqTjOB97ZhcGzoyAlWLFi0y9bFmzZpMn8+4fv9qgYGBNtdzHzp0SGFhYQoKCrpu7be6jWVXVkHt2vqu56uvvtI999wjDw8PBQUFKSQkRLNmzcp0nXWPHj20efNm6w8rGzZs0OnTpzN9h+Lj41W4cOFM63fhwoWb/l0ybN68Wa1atZK3t7cCAgIUEhJivY76Zt+hoKCgTD+gxcTEaNWqVZlqatWqlaQbf4ey89241fXOzt9r3LhxiouLU/ny5VW1alU999xz2rt373XrzIns7BdvVZ8+fXT58mUtX75ckhQdHa1du3bpkUceueHnSpcurWHDhmnOnDkqVKiQWrdurXfeecdmOzx06JCcnJxUqVKl6/aTsV1ce38BNzc3lSlTxua/C1LW+/Hs7nN79Oihhg0b6oknnlCRIkXUs2dPffrpp/wQAAB3OO72D9wB/Pz8FBYWpt9+++2WPpedx1Z16NBBXl5e+vTTT9WgQQN9+umncnJyst64Srpywy6LxaJvv/02y7tiZxzBzpDVXcnj4uLUtGlT+fn5ady4cYqIiJCHh4d+/vlnjRw5Mkf/KExPT1fVqlU1derULNtLlChxw8/fc889Cg8P16effqqHHnpIX375pS5fvmzzD3yLxaLPPvtM27Zt05dffqnVq1frscce0xtvvKFt27ZlWvdbkfH3vDak5VTGGA4fPjzLo8ZZLevav1VGHwsWLFBoaGimz197B/W8usP+rW5j2XW9+sxNnmK7adMmdezYUU2aNNHMmTNVtGhRubq6au7cuVq0aJHNvD169NCoUaO0dOlSPfPMM/r000/l7++vNm3aWOdJT09X4cKFtXDhwiyXd+0PKFl9hw4dOqSWLVuqYsWKmjp1qkqUKCE3Nzd98803evPNN3P8Hbr33ns1YsSILNszQl1WsvPduNX1zs7fq0mTJjp06JC++OILrVmzRnPmzNGbb76pd999N88ee5md/eKtqlSpkqKiovTxxx+rT58++vjjj+Xm5qbu3bvf9LNvvPGG+vXrZ13nIUOGaOLEidq2bZvNjTrzUlbbYHb3uZ6entq4caPWr1+vr7/+WqtWrdKSJUvUokULrVmzxmGfzAEAdzvCP3CHaN++vd5//31t3bpV9evXv+G8pUqVUnp6umJiYhQZGWmdfurUKcXFxVnvIC9J3t7eat++vZYuXaqpU6dqyZIlaty4scLCwqzzREREyBij0qVL3zAM3MiGDRt09uxZLVu2TE2aNLFOj42NzVS7dOUo9tV3UU9NTdXhw4dVrVo1m7r27Nmjli1b5vjZ7t27d9e0adOUkJCgJUuWKDw8XPfcc0+m+e655x7dc889evXVV7Vo0SL17t1bixcvznHYSEtL06JFi+Tl5WW9xCC3Mu5Q7urqaj1ye6siIiIkSYULF85xH1n1uXr1ap07d+66R//zYhvLS59//rk8PDy0evVqm8e5zZ07N9O8pUuXVt26dbVkyRINGjRIy5YtU+fOnW0+FxERobVr16phw4Y5fmTfl19+qaSkJK1cudLmCPm1p85f/R26+gyCs2fPZjrjISIiQhcuXMjV3/pG3428WO+sBAUF6dFHH9Wjjz6qCxcuqEmTJhozZswNv4+3so/Izn4xJ8vo06ePhg0bphMnTmjRokW6//77M52NcT1Vq1ZV1apV9eKLL2rLli1q2LCh3n33Xb3yyiuKiIhQenq69u3bpxo1amT5+YztIjo62uZpBsnJyYqNjc3WNnAr+1wnJye1bNlSLVu21NSpUzVhwgS98MILWr9+fZ7tWwAAeYvT/oE7xIgRI+Tt7a0nnnhCp06dytR+6NAhTZs2TZLUrl07SdJbb71lM0/G0Zr777/fZnqPHj10/PhxzZkzR3v27Ml0amuXLl3k7OyssWPHZjpiaozR2bNnb1p/xpGeqz+fnJysmTNn2sxXu3ZtBQcHa/bs2UpNTbVOX7hwYabg0r17d/3999+aPXt2puVdvnxZFy9evGldPXr0UFJSkubPn69Vq1ZlOgr377//ZlrnjH9c5/TU/7S0NA0ZMkT79+/XkCFDrJd15FbhwoXVrFkzvffeezpx4kSm9n/++eemfbRu3Vp+fn6aMGFCltdxZ6ePa3Xt2lXGGI0dOzZTW8bY5sU2lpecnZ1lsVhsHll4+PBhrVixIsv5e/TooW3btunDDz/UmTNnMn2HunfvrrS0NI0fPz7TZ1NTUzM9au16NUm236H4+PhMP0i0bNlSLi4umR4BOGPGjEx9du/eXVu3btXq1asztcXFxdl8B6+Vne9GXqz3ta7dFnx8fFS2bNmbfh+9vb1vaXk32y9ebxmSrrucXr16yWKx6L///a/+/PNP62VPN5KQkJDp71C1alU5OTlZ17lz585ycnLSuHHjMp0BkvE3atWqldzc3PT222/b/N0++OADxcfHZ/rvQlayu889d+5cpvbc7jcBAPmPI//AHSIiIkKLFi1Sjx49FBkZqT59+qhKlSpKTk7Wli1btHTpUuvzpqtXr66+ffvq/ffft55uv2PHDs2fP1+dO3fO9Fzydu3aydfXV8OHD5ezs7O6du2aadmvvPKKRo0apcOHD6tz587y9fVVbGysli9frieffFLDhw+/Yf0NGjRQYGCg+vbtqyFDhshisWjBggWZwoObm5vGjBmjwYMHq0WLFurevbsOHz6sefPmKSIiwuZo0yOPPKJPP/1U//nPf7R+/Xo1bNhQaWlpOnDggD799FPrc9JvpFatWipbtqxeeOEFJSUlZfoH/vz58zVz5kw98MADioiI0Pnz5zV79mz5+flZf2S5kfj4eH388ceSpEuXLungwYNatmyZDh06pJ49e2YZinLjnXfeUaNGjVS1alX1799fZcqU0alTp7R161b99ddf2rNnzw0/7+fnp1mzZumRRx5RrVq11LNnT4WEhOjo0aP6+uuv1bBhwyxD5I00b95cjzzyiN5++23FxMSoTZs2Sk9P16ZNm9S8eXMNGjQoT7axvHT//fdr6tSpatOmjR566CGdPn1a77zzjsqWLZvl9eXdu3fX8OHDNXz4cAUFBWU6stm0aVMNGDBAEydO1O7du3XffffJ1dVVMTExWrp0qaZNm6YHH3zwhjXdd999cnNzU4cOHTRgwABduHBBs2fPVuHChW1+7ClSpIj++9//6o033lDHjh3Vpk0b7dmzR99++60KFSpk8x167rnntHLlSrVv3179+vVTVFSULl68qF9//VWfffaZDh8+rEKFCmVZT3a+G3mx3teqVKmSmjVrpqioKAUFBemnn37SZ599pkGDBt3wc1FRUVq7dq2mTp2qsLAwlS5dWvXq1bvu/DfbL2alRo0acnZ21uTJkxUfHy93d3e1aNFChQsXlnTlMoc2bdpo6dKlCggIyFbg/v777zVo0CB169ZN5cuXV2pqqhYsWGBTU8Y+bPz48WrcuLG6dOkid3d37dy5U2FhYZo4caJCQkI0atQojR07Vm3atFHHjh0VHR2tmTNnqk6dOtn6ISK7+9xx48Zp48aNuv/++1WqVCmdPn1aM2fOVPHixfPsTCcAQD64nY8WAHBzf/zxh+nfv78JDw83bm5uxtfX1zRs2NBMnz7d5vFuKSkpZuzYsaZ06dLG1dXVlChRwowaNcpmnqv17t3b+vzn6/n8889No0aNjLe3t/H29jYVK1Y0AwcONNHR0dZ5bvR4u82bN5t77rnHeHp6mrCwMDNixAizevXqLB+N9fbbb5tSpUoZd3d3U7duXbN582YTFRVl2rRpYzNfcnKymTx5sqlcubJxd3c3gYGBJioqyowdO9bEx8ffbDiNMca88MILRpIpW7Zspraff/7Z9OrVy5QsWdK4u7ubwoULm/bt25uffvrppv1mPC4r4+Xj42PKlStnHn74YbNmzZosP5PbR/0Zc+V543369DGhoaHG1dXVFCtWzLRv39589tln1nkyHm92vce0rV+/3rRu3dr4+/sbDw8PExERYfr162ez3n379jXe3t6ZPpvVY9ZSU1PNa6+9ZipWrGjc3NxMSEiIadu2rdm1a5fNfNnZxrJyvUf9ZfVIuKZNm9o8MvJ6PvjgA1OuXDnj7u5uKlasaObOnZvlumVo2LBhlo/YvNr7779voqKijKenp/H19TVVq1Y1I0aMMMePH79p3cYYs3LlSlOtWjXj4eFhwsPDzeTJk82HH36Yad1TU1PN6NGjTWhoqPH09DQtWrQw+/fvN8HBweY///mPTZ/nz583o0aNMmXLljVubm6mUKFCpkGDBub111+3efb7tW7lu5Gb9b727/XKK6+YunXrmoCAAOPp6WkqVqxoXn31VZtas/o7HThwwDRp0sR4enraPPIwq20nw832i9d+X40xZvbs2aZMmTLG2dk5y31bxuNEn3zyySz7vNaff/5pHnvsMRMREWE8PDxMUFCQad68uVm7dm2meT/88ENTs2ZN676wadOm5rvvvrOZZ8aMGaZixYrG1dXVFClSxDz11FPm33//tZnnRvvx7Oxz161bZzp16mTCwsKMm5ubCQsLM7169cr0SEkAwJ3FYsxN7ooEALdBenq6QkJC1KVLlyxPOQVwY3FxcQoMDNQrr7yiF154wd7lFFhffPGFOnfurI0bN9o81hAAAHvjmn8At11iYmKmywE++ugjnTt3Ts2aNbNPUcBd5PLly5mmZdwDhO+Qfc2ePVtlypTh9HcAwB2Ha/4B3Hbbtm3T0KFD1a1bNwUHB+vnn3/WBx98oCpVquTqUVtAQbFkyRLNmzdP7dq1k4+Pj3788Ud98sknuu+++9SwYUN7l1cgLV68WHv37tXXX3+tadOm5fgJJQAA5BdO+wdw2x0+fFhDhgzRjh07rI+Ha9eunSZNmmS9cRaA6/v55581YsQI7d69WwkJCSpSpIi6du2qV155RT4+PvYur0CyWCzy8fFRjx499O6778rFheMrAIA7C+EfAAAAAAAHxzX/AAAAAAA4OMI/AAAAAAAOzuEvSEtPT9fx48fl6+vLzXcAAACAAswYo/PnzyssLExOThwHRcHi8OH/+PHjKlGihL3LAAAAAHCHOHbsmIoXL27vMoDbyuHDv6+vr6QrX3A/Pz87VwMAAADAXhISElSiRAlrRgAKEocP/xmn+vv5+RH+AQAAAHA5MAokLnQBAAAAAMDBEf4BAAAAAHBwhH8AAAAAABycw1/zDwAAAADZZYxRamqq0tLS7F0KcEPOzs5ycXHJ9j0sCP8AAAAAICk5OVknTpzQpUuX7F0KkC1eXl4qWrSo3Nzcbjov4R8AAABAgZeenq7Y2Fg5OzsrLCxMbm5uPBUAdyxjjJKTk/XPP/8oNjZW5cqVk5PTja/qJ/wDAAAAKPCSk5OVnp6uEiVKyMvLy97lADfl6ekpV1dXHTlyRMnJyfLw8Ljh/NzwDwAAAAD+v5sdPQXuJLeyvbJlAwAAAADg4Aj/AAAAAAA4OMI/AAAAAAC3wZgxY1SjRg27LJvwDwAAAAB5JP5Ssg6dvqBfjv6rQ/9cUPylZHuXZDfh4eF666237F1GJv369VPnzp3tXcZtx93+AQAAACAPHI+7rJGf79WmmDPWaU3KFdKkrtUUFuBpx8qyLzk5OVvPjMetMcYoLS3NrjVw5B8AAAC4Q8RfTtHRcxd19OxFxV8uuEeM70bxl5IzBX9J2hhzRv/7fG++ngGQnp6uKVOmqGzZsnJ3d1fJkiX16quvSpJ+/fVXtWjRQp6engoODtaTTz6pCxcuWD+bcRT81VdfVVhYmCpUqKDDhw/LYrFo2bJlat68uby8vFS9enVt3brVZrk//vijGjduLE9PT5UoUUJDhgzRxYsXJUnNmjXTkSNHNHToUFksFlksFuvnPv/8c1WuXFnu7u4KDw/XG2+8ke11DQ8P14QJE/TYY4/J19dXJUuW1Pvvv28zz7Fjx9S9e3cFBAQoKChInTp10uHDhyVdOe1+/vz5+uKLL6x1bdiwQQ8++KAGDRpk7eOZZ56RxWLRgQMHJF35UcTb21tr166VJCUlJWnIkCEqXLiwPDw81KhRI+3cudP6+Q0bNshisejbb79VVFSU3N3d9eOPP2Zan0OHDqlMmTIaNGiQjDHZHoecIPwDAAAAdpaebhRz6ryeXrhLTaZsUJPXNmjAgl2KPnleaen5GwiQN85cSM4U/DNsjDmjMxfyL/yPGjVKkyZN0ujRo7Vv3z4tWrRIRYoU0cWLF9W6dWsFBgZq586dWrp0qdauXWsTciVp3bp1io6O1nfffaevvvrKOv2FF17Q8OHDtXv3bpUvX169evVSamqqpCuhtU2bNuratav27t2rJUuW6Mcff7T2vWzZMhUvXlzjxo3TiRMndOLECUnSrl271L17d/Xs2VO//vqrxowZo9GjR2vevHnZXt833nhDtWvX1i+//KKnn35aTz31lKKjoyVJKSkpat26tXx9fbVp0yZt3rxZPj4+atOmjZKTkzV8+HB1795dbdq0sdbVoEEDNW3aVBs2bLAu44cfflChQoWs03bu3KmUlBQ1aNBAkjRixAh9/vnnmj9/vn7++WeVLVtWrVu31rlz52xq/d///qdJkyZp//79qlatmk3b3r171ahRIz300EOaMWOGzQ8k+cI4uPj4eCPJxMfH27sUAAAAIEuHz1wwVV5aZUqN/MrmVWn0tyb2nwv2Ls9h3CgbXL582ezbt89cvnw5R33/fORcpr/f1a9fjpzLbflZSkhIMO7u7mb27NmZ2t5//30TGBhoLlz4v23o66+/Nk5OTubkyZPGGGP69u1rihQpYpKSkqzzxMbGGklmzpw51mm///67kWT2799vjDHm8ccfN08++aTN8jZt2mScnJysY1iqVCnz5ptv2szz0EMPmXvvvddm2nPPPWcqVaqUrfUtVaqUefjhh63v09PTTeHChc2sWbOMMcYsWLDAVKhQwaSnp1vnSUpKMp6enmb16tXWde7UqZNNv3v37jUWi8WcPn3anDt3zri5uZnx48ebHj16GGOMeeWVV0yDBg2MMcZcuHDBuLq6moULF1o/n5ycbMLCwsyUKVOMMcasX7/eSDIrVqywWc7LL79sqlevbjZv3mwCAwPN66+/nq31vp5b2W458g8AAADYUWpauj7deUznk1IztV1MTtPC7UeUYudrhXFzfh6uN2z3vUl7Tu3fv19JSUlq2bJllm3Vq1eXt7e3dVrDhg2Vnp5uPVIuSVWrVs3yOv+rj1QXLVpUknT69GlJ0p49ezRv3jz5+PhYX61bt1Z6erpiY2NvWG/Dhg1tpjVs2FAxMTHZvib+6rosFotCQ0Nt6jp48KB8fX2tdQUFBSkxMVGHDh26bp9VqlRRUFCQfvjhB23atEk1a9ZU+/bt9cMPP0i6ciZAs2bNJF056yElJcVmPVxdXVW3bl3t37/fpt/atWtnWtbRo0d177336qWXXtKzzz6brXXOC9zwDwAAALCj84mp+iHmn+u2b4o5o6eaRSjI2/k2VoVbVcjHTU3KFdLGLE79b1KukAr55M9N9Dw9c38jwat/HLiaq+v//WCRcUp6enq6JOnChQsaMGCAhgwZkulzJUuWzHVNN3J1XRm1XV1XVFSUFi5cmOlzISEh1+3TYrGoSZMm2rBhg9zd3dWsWTNVq1ZNSUlJ+u2337RlyxYNHz78lmvNamxDQkIUFhamTz75RI899pj8/Pxuud+c4Mg/AAAAYEduLk4K9r5+MAz2cZObM8H/Tufv5aZJXaupSblCNtOblCukyV2ryd8rf8J/uXLl5OnpqXXr1mVqi4yM1J49e6w34ZOkzZs3y8nJSRUqVMjVcmvVqqV9+/apbNmymV4ZZxG4ubllOpofGRmpzZs320zbvHmzypcvL+c82M5r1aqlmJgYFS5cOFNd/v7+161LkvW6/w0bNqhZs2ZycnJSkyZN9NprrykpKcl6pD8iIkJubm4265GSkqKdO3eqUqVKN63R09NTX331lTw8PNS6dWudP38+1+udHYR/AAAAwI683V00oEnEddsHNImQjwcn7N4NwgI8Nb1XTa0b1lQrnm6gdcOaanqvmiqaj4/58/Dw0MiRIzVixAh99NFHOnTokLZt26YPPvhAvXv3loeHh/r27avffvtN69ev1+DBg/XII4+oSJEiuVruyJEjtWXLFg0aNEi7d+9WTEyMvvjiC5ubCYaHh2vjxo36+++/debMlTMinn32Wa1bt07jx4/XH3/8ofnz52vGjBk5Oqqeld69e6tQoULq1KmTNm3apNjYWG3YsEFDhgzRX3/9Za1r7969io6O1pkzZ5SSkiLpyhMK9u3bp99//12NGjWyTlu4cKFq165tPYrv7e2tp556Ss8995xWrVqlffv2qX///rp06ZIef/zxbNXp7e2tr7/+Wi4uLmrbtq3NExjyi93D/99//62HH35YwcHB8vT0VNWqVfXTTz9Z240xeumll1S0aFF5enqqVatWiomJsWPFAAAAQN6qWNRPA5qUyTT98UalVaXY7TklGHnD38tNEYV9VKNkoCIK++TbEf+rjR49Ws8++6xeeuklRUZGqkePHjp9+rS8vLy0evVqnTt3TnXq1NGDDz6oli1basaMGbleZrVq1fTDDz/ojz/+UOPGjVWzZk299NJLCgsLs84zbtw4HT58WBEREdZT7mvVqqVPP/1UixcvVpUqVfTSSy9p3Lhx6tevX65rkiQvLy9t3LhRJUuWVJcuXRQZGanHH39ciYmJ1tPr+/fvrwoVKqh27doKCQmxHsGvWrWqAgICVKNGDfn4+Ei6Ev7T0tKs1/tnmDRpkrp27apHHnlEtWrV0sGDB7V69WoFBgZmu1YfHx99++23Msbo/vvvtzlDIz9YjMnnhwnewL///quaNWuqefPmeuqppxQSEqKYmBhFREQoIuLKr5+TJ0/WxIkTNX/+fJUuXVqjR4/Wr7/+qn379snDw+Omy0hISJC/v7/i4+Nv27UUAAAAwK2Kv5ysf84n6ceYMzKSGpYtpCJ+HvL3zJ8bxRVEN8oGiYmJio2NVenSpbOVM4A7wa1st3Y9f2jy5MkqUaKE5s6da51WunRp6/83xuitt97Siy++qE6dOkmSPvroIxUpUkQrVqxQz549b3vNAAAAQH7w93STv6ebyhb2tXcpAByQXU/7X7lypWrXrq1u3bqpcOHCqlmzpmbPnm1tj42N1cmTJ9WqVSvrNH9/f9WrV09bt27Nss+kpCQlJCTYvAAAAAAAd7ZNmzbZPDrw2hdyx65H/v/880/NmjVLw4YN0/PPP6+dO3dqyJAhcnNzU9++fXXy5ElJynQziiJFiljbrjVx4kSNHTs232sHAAAAAOSd2rVra/fu3fYuw2HZNfynp6erdu3amjBhgiSpZs2a+u233/Tuu++qb9++Oepz1KhRGjZsmPV9QkKCSpQokSf1AgAAAADyh6enp8qWLWvvMhyWXU/7L1q0aKbnIEZGRuro0aOSpNDQUEnSqVOnbOY5deqUte1a7u7u8vPzs3kBAAAAAFCQ2TX8N2zYUNHR0TbT/vjjD5UqVUrSlZv/hYaGat26ddb2hIQEbd++XfXr17+ttQIAAAAAcLey62n/Q4cOVYMGDTRhwgR1795dO3bs0Pvvv6/3339fkmSxWPTMM8/olVdeUbly5ayP+gsLC1Pnzp3tWToAAAAAAHcNu4b/OnXqaPny5Ro1apTGjRun0qVL66233lLv3r2t84wYMUIXL17Uk08+qbi4ODVq1EirVq3i2ZsAAAAAAGSTxRhj7F1EfkpISJC/v7/i4+O5/h8AAAAowG6UDRITExUbG6vSpUtzoBF3jVvZbu16zT8AAAAAIH/069ePy6VzwWKxaMWKFfYuI88Q/gEAAAAgr1z+Vzrzh/TXT9KZmCvvHVB+/LAwZswY1ahRI0/7vJOXe7vZ9Zp/AAAAAHAY8X9LXwyS/vz+/6ZFtJQ6Tpf8i9mvrjyUlpYmi8Vi7zKQAxz5BwAAAIDcuvxv5uAvSYfWSSsH5+sZAJ999pmqVq0qT09PBQcHq1WrVrp48aK1/fXXX1fRokUVHBysgQMHKiUlxdr277//qk+fPgoMDJSXl5fatm2rmJgYa/u8efMUEBCglStXqlKlSnJ3d9djjz2m+fPn64svvpDFYpHFYtGGDRskSceOHVP37t0VEBCgoKAgderUSYcPH7b2t2HDBtWtW1fe3t4KCAhQw4YNdeTIEc2bN09jx47Vnj17rH3OmzfvputusVg0a9YstW3bVp6enipTpow+++wzm3lGjhyp8uXLy8vLS2XKlNHo0aOtY3Cz5Z45c0YPPPCAvLy8VK5cOa1cudJm7Hr37q2QkBB5enqqXLlymjt3bnb+ZHbBkX8AAAAAyK2L/2QO/hkOrbvS7hmY54s9ceKEevXqpSlTpuiBBx7Q+fPntWnTJmXc1339+vUqWrSo1q9fr4MHD6pHjx6qUaOG+vfvL+nK6fsxMTFauXKl/Pz8NHLkSLVr10779u2Tq6urJOnSpUuaPHmy5syZo+DgYBUtWlSXL19WQkKCNewGBQUpJSVFrVu3Vv369bVp0ya5uLjolVdeUZs2bbR37145OTmpc+fO6t+/vz755BMlJydrx44dslgs6tGjh3777TetWrVKa9eulST5+/tnawxGjx6tSZMmadq0aVqwYIF69uypX3/9VZGRkZIkX19fzZs3T2FhYfr111/Vv39/+fr6asSIETdd7tixYzVlyhS99tprmj59unr37q0jR44oKChIo0eP1r59+/Ttt9+qUKFCOnjwoC5fvpwHf9X8QfgHAAAAgNxKTMhdew6dOHFCqamp6tKli0qVKiVJqlq1qrU9MDBQM2bMkLOzsypWrKj7779f69atU//+/a2hf/PmzWrQoIEkaeHChSpRooRWrFihbt26SZJSUlI0c+ZMVa9e3dqvp6enkpKSFBoaap328ccfKz09XXPmzLFeGjB37lwFBARow4YNql27tuLj49W+fXtFRERIkjWgS5KPj49cXFxs+syObt266YknnpAkjR8/Xt99952mT5+umTNnSpJefPFF67zh4eEaPny4Fi9erBEjRsjT0/OGy+3Xr5969eolSZowYYLefvtt7dixQ23atNHRo0dVs2ZN1a5d29r3nYzT/gEAAAAgtzxu8ljxm7XnUPXq1dWyZUtVrVpV3bp10+zZs/Xvv/93iUHlypXl7OxsfV+0aFGdPn1akrR//365uLioXr161vbg4GBVqFBB+/fvt05zc3NTtWrVblrLnj17dPDgQfn6+srHx0c+Pj4KCgpSYmKiDh06pKCgIPXr10+tW7dWhw4dNG3aNJ04cSLXY1C/fv1M76+uf8mSJWrYsKFCQ0Pl4+OjF198UUePHs1W31evt7e3t/z8/Kzj99RTT2nx4sWqUaOGRowYoS1btuR6XfIT4R8AAAAAcss75MrN/bIS0fJKez5wdnbWd999p2+//VaVKlXS9OnTVaFCBcXGxkqS9dT9DBaLRenp6be0DE9Pz2zd5O/ChQuKiorS7t27bV5//PGHHnroIUlXzgTYunWrGjRooCVLlqh8+fLatm3bLdVzK7Zu3arevXurXbt2+uqrr/TLL7/ohRdeUHJycrY+f6Pxa9u2rY4cOaKhQ4fq+PHjatmypYYPH57n65BXCP8AAAAAkFuegVfu6n/tDwAZd/vPh+v9M1gsFjVs2FBjx47VL7/8Ijc3Ny1fvvymn4uMjFRqaqq2b99unXb27FlFR0erUqVKN/ysm5ub0tLSbKbVqlVLMTExKly4sMqWLWvzuvo6+po1a2rUqFHasmWLqlSpokWLFl23z+y49seDbdu2WS8n2LJli0qVKqUXXnhBtWvXVrly5XTkyJGbrkt2hYSEqG/fvvr444/11ltv6f33389RP7cD1/wDAAAAQF7wLyY9+MGVm/slJlw51d87JF+D//bt27Vu3Trdd999Kly4sLZv365//vlHkZGR2rt37w0/W65cOXXq1En9+/fXe++9J19fX/3vf/9TsWLF1KlTpxt+Njw8XKtXr1Z0dLSCg4Pl7++v3r1767XXXlOnTp00btw4FS9eXEeOHNGyZcs0YsQIpaSk6P3331fHjh0VFham6OhoxcTEqE+fPtY+Y2NjtXv3bhUvXly+vr5yd3e/6RgsXbpUtWvXVqNGjbRw4ULt2LFDH3zwgXUdjx49qsWLF6tOnTr6+uuvM/0wktPlvvTSS4qKilLlypWVlJSkr776yuYeBncajvwDAAAAQF7xDJQKlZeK177yv/kY/CXJz89PGzduVLt27VS+fHm9+OKLeuONN9S2bdtsfX7u3LmKiopS+/btVb9+fRlj9M0332Q63f1a/fv3V4UKFVS7dm2FhIRo8+bN8vLy0saNG1WyZEl16dJFkZGRevzxx5WYmCg/Pz95eXnpwIED6tq1q8qXL68nn3xSAwcO1IABAyRJXbt2VZs2bdS8eXOFhITok08+ydY6jB07VosXL1a1atX00Ucf6ZNPPrGeudCxY0cNHTpUgwYNUo0aNbRlyxaNHj3a5vM5Xa6bm5tGjRqlatWqqUmTJnJ2dtbixYuz9Vl7sJiMZ0A4qISEBPn7+ys+Pl5+fvlzkw0AAAAAd74bZYPExETFxsaqdOnS8vDwsFOFuFUWi0XLly9X586d7V2KXdzKdsuRfwAAAAAAHBzhHwAAAABwx1m4cKH1kYHXvipXrmzv8u463PAPAAAAAHDH6dixo+rVq5dlW8Y9CRz8KvY8RfgHAAAAANxxfH195evra+8yHAan/QMAAAAA4OAI/wAAAAAAODjCPwAAAAAADo7wDwAAAACAgyP8AwAAAADg4Aj/AAAAAOCA+vXrp86dO9u7jLuWxWLRihUr7F1GniH8AwAAAABuSX78sDBmzBjVqFEjT/u8k5d7u7nYuwAAAAAAcBTxSfE6l3hO55PPy9fNV0EeQfJ397d3WXkmLS1NFovF3mUgBzjyDwAAAAB54OTFkxqxcYQ6ruio3t/0VscVHTVy40idvHgyX5f72WefqWrVqvL09FRwcLBatWqlixcvWttff/11FS1aVMHBwRo4cKBSUlKsbf/++6/69OmjwMBAeXl5qW3btoqJibG2z5s3TwEBAVq5cqUqVaokd3d3PfbYY5o/f76++OILWSwWWSwWbdiwQZJ07Ngxde/eXQEBAQoKClKnTp10+PBha38bNmxQ3bp15e3trYCAADVs2FBHjhzRvHnzNHbsWO3Zs8fa57x582667haLRbNmzVLbtm3l6empMmXK6LPPPrOZZ+TIkSpfvry8vLxUpkwZjR492joGN1vumTNn9MADD8jLy0vlypXTypUrbcaud+/eCgkJkaenp8qVK6e5c+fetObk5GQNGjRIRYsWlYeHh0qVKqWJEyda2+Pi4jRgwAAVKVJEHh4eqlKlir766qub9nszHPkHAAAAgFyKT4rXy1te1pbjW2ymbz6+WWO2jNHkJpPz5QyAEydOqFevXpoyZYoeeOABnT9/Xps2bZIxRpK0fv16FS1aVOvXr9fBgwfVo0cP1ahRQ/3795d05fT9mJgYrVy5Un5+fho5cqTatWunffv2ydXVVZJ06dIlTZ48WXPmzFFwcLCKFi2qy5cvKyEhwRp2g4KClJKSotatW6t+/fratGmTXFxc9Morr6hNmzbau3evnJyc1LlzZ/Xv31+ffPKJkpOTtWPHDlksFvXo0UO//fabVq1apbVr10qS/P2zN16jR4/WpEmTNG3aNC1YsEA9e/bUr7/+qsjISEmSr6+v5s2bp7CwMP3666/q37+/fH19NWLEiJsud+zYsZoyZYpee+01TZ8+Xb1799aRI0cUFBSk0aNHa9++ffr2229VqFAhHTx4UJcvX75pvW+//bZWrlypTz/9VCVLltSxY8d07NgxSVJ6erratm2r8+fP6+OPP1ZERIT27dsnZ2fnbI3FjRD+AQAAACCXziWeyxT8M2w+vlnnEs/lW/hPTU1Vly5dVKpUKUlS1apVre2BgYGaMWOGnJ2dVbFiRd1///1at26d+vfvbw39mzdvVoMGDSRJCxcuVIkSJbRixQp169ZNkpSSkqKZM2eqevXq1n49PT2VlJSk0NBQ67SPP/5Y6enpmjNnjvXSgLlz5yogIEAbNmxQ7dq1FR8fr/bt2ysiIkKSrAFdknx8fOTi4mLTZ3Z069ZNTzzxhCRp/Pjx+u677zR9+nTNnDlTkvTiiy9a5w0PD9fw4cO1ePFijRgxQp6enjdcbr9+/dSrVy9J0oQJE/T2229rx44datOmjY4ePaqaNWuqdu3a1r6z4+jRoypXrpwaNWoki8Vi/btJ0tq1a7Vjxw7t379f5cuXlySVKVPmlsbjejjtHwAAAABy6Xzy+Vy151T16tXVsmVLVa1aVd26ddPs2bP177//WtsrV65sc9S4aNGiOn36tCRp//79cnFxUb169aztwcHBqlChgvbv32+d5ubmpmrVqt20lj179ujgwYPy9fWVj4+PfHx8FBQUpMTERB06dEhBQUHq16+fWrdurQ4dOmjatGk6ceJErsegfv36md5fXf+SJUvUsGFDhYaGysfHRy+++KKOHj2arb6vXm9vb2/5+flZx++pp57S4sWLVaNGDY0YMUJbtmT948+1+vXrp927d6tChQoaMmSI1qxZY23bvXu3ihcvbg3+eYnwDwAAAAC55Ovmm6v2nHJ2dtZ3332nb7/9VpUqVdL06dNVoUIFxcbGSpL11P0MFotF6enpt7QMT0/PbN3k78KFC4qKitLu3bttXn/88YceeughSVfOBNi6dasaNGigJUuWqHz58tq2bdst1XMrtm7dqt69e6tdu3b66quv9Msvv+iFF15QcnJytj5/o/Fr27atjhw5oqFDh+r48eNq2bKlhg8fftM+a9WqpdjYWI0fP16XL19W9+7d9eCDD0q6Mtb5hfAPAAAAALkU5BGkhmENs2xrGNZQQR5B+bZsi8Wihg0bauzYsfrll1/k5uam5cuX3/RzkZGRSk1N1fbt263Tzp49q+joaFWqVOmGn3Vzc1NaWprNtFq1aikmJkaFCxdW2bJlbV5XX0dfs2ZNjRo1Slu2bFGVKlW0aNGi6/aZHdf+eLBt2zbr5QRbtmxRqVKl9MILL6h27doqV66cjhw5ctN1ya6QkBD17dtXH3/8sd566y29//772fqcn5+fevToodmzZ2vJkiX6/PPPde7cOVWrVk1//fWX/vjjjxzVcyOEfwAAAADIJX93f41pMCbTDwANwxpqTIMx+fa4v+3bt2vChAn66aefdPToUS1btkz//POPzbX011OuXDl16tRJ/fv3148//qg9e/bo4YcfVrFixdSpU6cbfjY8PFx79+5VdHS0zpw5o5SUFPXu3VuFChVSp06dtGnTJsXGxmrDhg0aMmSI/vrrL8XGxmrUqFHaunWrjhw5ojVr1igmJsZaa3h4uGJjY7V7926dOXNGSUlJ2RqDpUuX6sMPP9Qff/yhl19+WTt27NCgQYOs63j06FEtXrxYhw4d0ttvv53ph5GcLvell17SF198oYMHD+r333/XV199la1xnzp1qj755BMdOHBAf/zxh5YuXarQ0FAFBASoadOmatKkibp27arvvvtOsbGx+vbbb7Vq1aps1XQjhH8AAAAAyAOh3qGa3GSyVnZeqYXtFmpl55Wa3GSyQr1v7QZ2t8LPz08bN25Uu3btVL58eb344ot644031LZt22x9fu7cuYqKilL79u1Vv359GWP0zTffZDrd/Vr9+/dXhQoVVLt2bYWEhGjz5s3y8vLSxo0bVbJkSXXp0kWRkZF6/PHHlZiYKD8/P3l5eenAgQPq2rWrypcvryeffFIDBw7UgAEDJEldu3ZVmzZt1Lx5c4WEhOiTTz7J1jqMHTtWixcvVrVq1fTRRx/pk08+sZ650LFjRw0dOlSDBg1SjRo1tGXLFo0ePdrm8zldrpubm0aNGqVq1aqpSZMmcnZ21uLFi2/6OV9fX02ZMkW1a9dWnTp1dPjwYX3zzTdycroSzz///HPVqVNHvXr1UqVKlTRixIgcn5lwNYvJeAaEg0pISJC/v7/i4+Pl5+dn73IAAAAA2MmNskFiYqJiY2NVunRpeXh42KlC3CqLxaLly5erc+fO9i7FLm5lu+XIPwAAAAAADo7wDwAAAAC44yxcuND6yMBrX5UrV7Z3edc1YcKE69ad3csx8oOL3ZYMAAAAAMB1dOzYUfXq1cuyLeOeBHfiVez/+c9/1L179yzb8vNRfjdD+AcAAAAA3HF8fX3l6+tr7zJuWVBQkIKC8u/RjjnFaf8AAAAAADg4wj8AAAAAAA6O8A8AAAAAgIMj/AMAAAAA4OAI/wAAAAAAODjCPwAAAAA4qMOHD8tisWj37t2SpA0bNshisSguLs6udc2bN08BAQF2raGgIfwDAAAAAPJNeHi43nrrLXuXUeC52LsAAAAAAHAUqfHxSjt7Vunnz8vJ10/OwUFy8fe3d1kAR/4BAAAAIC+knDipv4c9qz/b3a/DPXrqz3bt9Pezw5Vy4mS+LnfVqlVq1KiRAgICFBwcrPbt2+vQoUO57jfj1PwVK1aoXLly8vDwUOvWrXXs2DHrPIcOHVKnTp1UpEgR+fj4qE6dOlq7dq21vVmzZjpy5IiGDh0qi8Uii8Vis4zVq1crMjJSPj4+atOmjU6cOGFt27Bhg+rWrStvb28FBASoYcOGOnLkSK7Xq6Ai/AMAAABALqXGx+v4iy/q0ubNNtMv/fijjo8erdT4+Hxb9sWLFzVs2DD99NNPWrdunZycnPTAAw8oPT09131funRJr776qj766CNt3rxZcXFx6tmzp7X9woULateundatW6dffvlFbdq0UYcOHXT06FFJ0rJly1S8eHGNGzdOJ06csAn3ly5d0uuvv64FCxZo48aNOnr0qIYPHy5JSk1NVefOndW0aVPt3btXW7du1ZNPPpnpxwNkH6f9AwAAAEAupZ09myn4Z7j0449KO3s2307/79q1q837Dz/8UCEhIdq3b598fHxy1XdKSopmzJihevXqSZLmz5+vyMhI7dixQ3Xr1lX16tVVvXp16/zjx4/X8uXLtXLlSg0aNEhBQUFydnaWr6+vQkNDM/X97rvvKiIiQpI0aNAgjRs3TpKUkJCg+Ph4tW/f3toeGRmZq3Up6DjyDwAAAAC5lH7+/E3aL+TbsmNiYtSrVy+VKVNGfn5+Cg8PlyTr0ffccHFxUZ06dazvK1asqICAAO3fv1/SlSP/w4cPV2RkpAICAuTj46P9+/dna9leXl7WYC9JRYsW1enTpyVJQUFB6tevn1q3bq0OHTpo2rRpNmcN4NYR/gEAAAAgl5x8fW/Snrsj8DfSoUMHnTt3TrNnz9b27du1fft2SVJycnK+LTPD8OHDtXz5ck2YMEGbNm3S7t27VbVq1Wwt29XV1ea9xWKRMcb6fu7cudq6dasaNGigJUuWqHz58tq2bVuer0NBQfgHAAAAgFxyDg6WV6NGWbZ5NWok5+DgfFnu2bNnFR0drRdffFEtW7ZUZGSk/v333zzrPzU1VT/99JP1fXR0tOLi4qyn4G/evFn9+vXTAw88oKpVqyo0NFSHDx+26cPNzU1paWk5Wn7NmjU1atQobdmyRVWqVNGiRYtyvC4FHeEfAAAAAHLJxd9fYePHZ/oBwKtRI4W9Mj7frvcPDAxUcHCw3n//fR08eFDff/+9hg0blmf9u7q6avDgwdq+fbt27dqlfv366Z577lHdunUlSeXKldOyZcu0e/du7dmzRw899FCmGw2Gh4dr48aN+vvvv3XmzJlsLTc2NlajRo3S1q1bdeTIEa1Zs0YxMTFc958L3PAPAAAAAPKAa9FQFXvjdaWdPav08xfk5Osj5+DgfAv+kuTk5KTFixdryJAhqlKliipUqKC3335bzZo1y5P+vby8NHLkSD300EP6+++/1bhxY33wwQfW9qlTp+qxxx5TgwYNVKhQIY0cOVIJCQk2fYwbN04DBgxQRESEkpKSbE7tv9FyDxw4oPnz5+vs2bMqWrSoBg4cqAEDBuTJehVEFpOdkb+LJSQkyN/fX/Hx8fLz87N3OQAAAADs5EbZIDExUbGxsSpdurQ8PDzsVOGdZd68eXrmmWcUFxdn71JwHbey3XLaPwAAAAAADo7wDwAAAAAFUNu2beXj45Pla8KECfYuD3mMa/4BAAAAoACaM2eOLl++nGVbUFCQgoKC1K9fv9tbFPIN4R8AAAAACqBixYrZuwTcRpz2DwAAAACAgyP8AwAAAADg4Aj/AAAAAAA4OMI/AAAAAAAOjvAPAAAAAICDI/wDAAAAgIM6fPiwLBaLdu/eLUnasGGDLBaL4uLi7FrXvHnzFBAQYNcaChrCPwAAAAAg34SHh+utt96ydxkFnou9CwAAAAAAR5F4MUWXzycr+XKq3Dxd5OnrJg9vV3uXBXDkHwAAAADywoVziVoz53ctGrNdn03epUVjtmvNnN914Vxivi531apVatSokQICAhQcHKz27dvr0KFDue4349T8FStWqFy5cvLw8FDr1q117Ngx6zyHDh1Sp06dVKRIEfn4+KhOnTpau3attb1Zs2Y6cuSIhg4dKovFIovFYrOM1atXKzIyUj4+PmrTpo1OnDhhbduwYYPq1q0rb29vBQQEqGHDhjpy5MhN696zZ4+aN28uX19f+fn5KSoqSj/99JO1ffPmzWrWrJm8vLwUGBio1q1b699//83NUN0VCP8AAAAAkEuJF1P0/YIDOrb/nM30Y/vP6fsFB5R4MSXfln3x4kUNGzZMP/30k9atWycnJyc98MADSk9Pz3Xfly5d0quvvqqPPvpImzdvVlxcnHr27Gltv3Dhgtq1a6d169bpl19+UZs2bdShQwcdPXpUkrRs2TIVL15c48aN04kTJ2zC/aVLl/T6669rwYIF2rhxo44eParhw4dLklJTU9W5c2c1bdpUe/fu1datW/Xkk09m+vEgK71791bx4sW1c+dO7dq1S//73//k6nrl7Ivdu3erZcuWqlSpkrZu3aoff/xRHTp0UFpaWq7H6k7Haf8AAAAAkEuXzydnCv4Zju0/p8vnk/Pt9P+uXbvavP/www8VEhKiffv2ycfHJ1d9p6SkaMaMGapXr54kaf78+YqMjNSOHTtUt25dVa9eXdWrV7fOP378eC1fvlwrV67UoEGDFBQUJGdnZ/n6+io0NDRT3++++64iIiIkSYMGDdK4ceMkSQkJCYqPj1f79u2t7ZGRkdmq+ejRo3ruuedUsWJFSVK5cuWsbVOmTFHt2rU1c+ZM67TKlSvf6rDclTjyDwAAAAC5lHw5NVftuRETE6NevXqpTJky8vPzU3h4uCRZj77nhouLi+rUqWN9X7FiRQUEBGj//v2Srhz5Hz58uCIjIxUQECAfHx/t378/W8v28vKyBntJKlq0qE6fPi1JCgoKUr9+/dS6dWt16NBB06ZNszlr4EaGDRumJ554Qq1atdKkSZNsLoHIOPJfEBH+AQAAACCX3DxvfFL1zdpzo0OHDjp37pxmz56t7du3a/v27ZKk5OTkfFtmhuHDh2v58uWaMGGCNm3apN27d6tq1arZWnbGqfgZLBaLjDHW93PnztXWrVvVoEEDLVmyROXLl9e2bdtu2u+YMWP0+++/6/7779f333+vSpUqafny5ZIkT0/PW1xDx0H4BwAAAIBc8vR1U4nIoCzbSkQGydPXLV+We/bsWUVHR+vFF19Uy5YtFRkZmac3r0tNTbW5WV50dLTi4uKsp+Bv3rxZ/fr10wMPPKCqVasqNDRUhw8ftunDzc0tx9fU16xZU6NGjdKWLVtUpUoVLVq0KFufK1++vIYOHao1a9aoS5cumjt3riSpWrVqWrduXY5qudsR/gEAAAAglzy8XdXikYqZfgAoERmkFn0q5tv1/oGBgQoODtb777+vgwcP6vvvv9ewYcPyrH9XV1cNHjxY27dv165du9SvXz/dc889qlu3rqQr19MvW7ZMu3fv1p49e/TQQw9lutFgeHi4Nm7cqL///ltnzpzJ1nJjY2M1atQobd26VUeOHNGaNWsUExNz0+v+L1++rEGDBmnDhg06cuSINm/erJ07d1o/N2rUKO3cuVNPP/209u7dqwMHDmjWrFnZrutuxg3/AAAAACAP+AR56L4nKuvy+WQlX06Vm6eLPH3d8i34S5KTk5MWL16sIUOGqEqVKqpQoYLefvttNWvWLE/69/Ly0siRI/XQQw/p77//VuPGjfXBBx9Y26dOnarHHntMDRo0UKFChTRy5EglJCTY9DFu3DgNGDBAERERSkpKsjm1/0bLPXDggObPn6+zZ8+qaNGiGjhwoAYMGHDDzzk7O+vs2bPq06ePTp06pUKFCqlLly4aO3aspCtnBKxZs0bPP/+86tatK09PT9WrV0+9evXKwejcXSwmOyN/F0tISJC/v7/i4+Pl5+dn73IAAAAA2MmNskFiYqJiY2NVunRpeXh42KnCO8u8efP0zDPPKC4uzt6l4DpuZbvltH8AAAAAABwc4R8AAAAACqC2bdvKx8cny9eECRPsXd51Va5c+bp1L1y40N7l3bG45h8AAAAACqA5c+bo8uXLWbYFBQUpKChI/fr1u71FZcM333yjlJSULNuKFClym6u5exD+AQAAAKAAKlasmL1LyJFSpUrZu4S7kl1P+x8zZowsFovNq2LFitb2xMREDRw4UMHBwfLx8VHXrl116tQpO1YMAAAAwJE5+P3Q4WBuZXu1+zX/lStX1okTJ6yvH3/80do2dOhQffnll1q6dKl++OEHHT9+XF26dLFjtQAAAAAckavrlcfxXbp0yc6VANmXsb1mbL83YvfT/l1cXBQaGpppenx8vD744AMtWrRILVq0kCTNnTtXkZGR2rZtm+65557bXSoAAAAAB+Xs7KyAgACdPn1a0pXnzFssFjtXBWTNGKNLly7p9OnTCggIkLOz800/Y/fwHxMTo7CwMHl4eKh+/fqaOHGiSpYsqV27diklJUWtWrWyzluxYkWVLFlSW7duvW74T0pKUlJSkvV9QkJCvq8DAAAAgLtfxkHJjB8AgDtdQEBAlgfTs2LX8F+vXj3NmzdPFSpU0IkTJzR27Fg1btxYv/32m06ePCk3NzcFBATYfKZIkSI6efLkdfucOHGixo4dm8+VAwAAAHA0FotFRYsWVeHCha97N3ngTuHq6pqtI/4Z7Br+27Zta/3/1apVU7169VSqVCl9+umn8vT0zFGfo0aN0rBhw6zvExISVKJEiVzXCgAAAKBgcHZ2vqVQBdwN7H7Dv6sFBASofPnyOnjwoEJDQ5WcnKy4uDibeU6dOnXD0xrc3d3l5+dn8wIAAAAAoCC7o8L/hQsXdOjQIRUtWlRRUVFydXXVunXrrO3R0dE6evSo6tevb8cqAQAAAAC4u9j1tP/hw4erQ4cOKlWqlI4fP66XX35Zzs7O6tWrl/z9/fX4449r2LBhCgoKkp+fnwYPHqz69etzp38AAAAAAG6BXcP/X3/9pV69euns2bMKCQlRo0aNtG3bNoWEhEiS3nzzTTk5Oalr165KSkpS69atNXPmTHuWDAAAAADAXcdijDH2LiI/JSQkyN/fX/Hx8Vz/DwAAABRgZAMUZHfUNf8AAAAAACDvEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHR/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHR/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHR/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHR/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHR/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHR/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAeX6/CfmJiYF3UAAAAAAIB8kqPwn56ervHjx6tYsWLy8fHRn3/+KUkaPXq0PvjggzwtEAAAAAAA5E6Owv8rr7yiefPmacqUKXJzc7NOr1KliubMmZOjQiZNmiSLxaJnnnnGOi0xMVEDBw5UcHCwfHx81LVrV506dSpH/QMAAAAAUFDlKPx/9NFHev/999W7d285Oztbp1evXl0HDhy45f527typ9957T9WqVbOZPnToUH355ZdaunSpfvjhBx0/flxdunTJSckAAAAAABRYOQr/f//9t8qWLZtpenp6ulJSUm6prwsXLqh3796aPXu2AgMDrdPj4+P1wQcfaOrUqWrRooWioqI0d+5cbdmyRdu2bctJ2QAAAAAAFEg5Cv+VKlXSpk2bMk3/7LPPVLNmzVvqa+DAgbr//vvVqlUrm+m7du1SSkqKzfSKFSuqZMmS2rp163X7S0pKUkJCgs0LAAAAAICCzCUnH3rppZfUt29f/f3330pPT9eyZcsUHR2tjz76SF999VW2+1m8eLF+/vln7dy5M1PbyZMn5ebmpoCAAJvpRYoU0cmTJ6/b58SJEzV27Nhs1wAAAAAAgKPL0ZH/Tp066csvv9TatWvl7e2tl156Sfv379eXX36pe++9N1t9HDt2TP/973+1cOFCeXh45KSMLI0aNUrx8fHW17Fjx/KsbwAAAAAA7kY5OvIvSY0bN9Z3332X4wXv2rVLp0+fVq1atazT0tLStHHjRs2YMUOrV69WcnKy4uLibI7+nzp1SqGhodft193dXe7u7jmuCwAAAAAAR5Pj8J9bLVu21K+//moz7dFHH1XFihU1cuRIlShRQq6urlq3bp26du0qSYqOjtbRo0dVv359e5QMAAAAAMBdKUfhPzAwUBaLJdN0i8UiDw8PlS1bVv369dOjjz563T58fX1VpUoVm2ne3t4KDg62Tn/88cc1bNgwBQUFyc/PT4MHD1b9+vV1zz335KRsAAAAAAAKpBzf8O/VV19V27ZtVbduXUnSjh07tGrVKg0cOFCxsbF66qmnlJqaqv79++e4uDfffFNOTk7q2rWrkpKS1Lp1a82cOTPH/QEAAAAAUBBZjDHmVj/UtWtX3XvvvfrPf/5jM/29997TmjVr9Pnnn2v69Ol6//33M53af7slJCTI399f8fHx8vPzs2stAAAAAOyHbICCLEd3+1+9erVatWqVaXrLli21evVqSVK7du30559/5q46AAAAAACQazkK/0FBQfryyy8zTf/yyy8VFBQkSbp48aJ8fX1zVx0AAAAAAMi1HF3zP3r0aD311FNav3699Zr/nTt36ptvvtG7774rSfruu+/UtGnTvKsUAAAAAADkSI6u+ZekzZs3a8aMGYqOjpYkVahQQYMHD1aDBg3ytMDc4roeAAAAABLZAAVbjsP/3YIvOAAAAACJbICCLUen/V8tMTFRycnJNtP4IgEAAAAAcOfI0Q3/Ll26pEGDBqlw4cLy9vZWYGCgzQsAAAAAANw5chT+n3vuOX3//feaNWuW3N3dNWfOHI0dO1ZhYWH66KOP8rpGAAAAAACQCzk67f/LL7/URx99pGbNmunRRx9V48aNVbZsWZUqVUoLFy5U796987pOAAAAAACQQzk68n/u3DmVKVNG0pXr+8+dOydJatSokTZu3Jh31QEAAAAAgFzLUfgvU6aMYmNjJUkVK1bUp59+KunKGQEBAQF5VhwAAAAAAMi9HIX/Rx99VHv27JEk/e9//9M777wjDw8PDR06VM8991yeFggAAAAAAHLHYowxue3kyJEj2rVrl8qWLatq1arlRV15hmd5AgAAAJDIBijYbvnIf0pKilq2bKmYmBjrtFKlSqlLly53XPAHAAAAAAA5CP+urq7au3dvftQCAAAAAADyQY6u+X/44Yf1wQcf5HUtAAAAAAAgH7jk5EOpqan68MMPtXbtWkVFRcnb29umferUqXlSHAAAAAAAyL0chf/ffvtNtWrVkiT98ccfNm0WiyX3VQEAAAAAgDyTo/C/fv36vK4DAAAAAADkkxxd85/h4MGDWr16tS5fvixJyoOnBgIAAAAAgDyWo/B/9uxZtWzZUuXLl1e7du104sQJSdLjjz+uZ599Nk8LBAAAAAAAuZOj8D906FC5urrq6NGj8vLysk7v0aOHVq1alWfFAQAAAACA3MvRNf9r1qzR6tWrVbx4cZvp5cqV05EjR/KkMAAAAAAAkDdydOT/4sWLNkf8M5w7d07u7u65LgoAAAAAAOSdHIX/xo0b66OPPrK+t1gsSk9P15QpU9S8efM8Kw4AAAAAAORejk77nzJlilq2bKmffvpJycnJGjFihH7//XedO3dOmzdvzusaAQAAAABALuToyH+VKlX0xx9/qFGjRurUqZMuXryoLl266JdfflFERERe1wgAAAAAAHLBYowx9i4iPyUkJMjf31/x8fHy8/OzdzkAAAAA7IRsgIIsR0f+y5YtqzFjxigmJiav6wEAAADgIC6lXNI/l/5RXGKcvUsBCrwchf+BAwfq66+/VoUKFVSnTh1NmzZNJ0+ezOvaAAAAANyFktOSFfNvjF7e8rJ6fd1LA9YO0Noja3Uu8Zy9SwMKrFyd9v/HH39o4cKF+uSTTxQbG6vmzZvr4YcfVp8+ffKyxlzh1B4AAADg9tp9erceXfWoUk2qzfReFXtpUI1B8nO3z7/LyQYoyPLsmv9t27bpqaee0t69e5WWlpYXXeYJvuAAAADA7XPu8jk9seYJxcRlfYnwys4rVdq/9G2u6gqyAQqyHD3q72o7duzQokWLtGTJEiUkJKhbt255URcAAACAu1BCcsJ1g78k/XL6F7uFf6Agy1H4v/Z0/xYtWmjy5Mnq0qWLfHx88rpGAAAAAHcJJ8uNbyvm7ux+myoBcLUchf+KFSuqTp06GjhwoHr27KkiRYrkdV0AAAAA7kL+7v6KKhKlXad2ZWpzsjipWkg1O1QFIEfhPzo6WuXKlcvrWgAAAADc5fzd/TX6ntHq820fJSQn2LSNqjtKwR7BdqoMKNhydcO/Xbt2af/+/ZKkSpUqqVatWnlWWF7hph4AAADA7WWM0fELx7X68GptPbFVRb2LqlfFXirhW0I+bva7TJhsgIIsR+H/9OnT6tGjh3744QcFBARIkuLi4tS8eXMtXrxYISEheV1njvEFBwAAAOzDGKPE1ES5OrvKxSnX9xrPNbIBCrIb343jOgYPHqwLFy7o999/17lz53Tu3Dn99ttvSkhI0JAhQ/K6RgAAADia9HQp/m/pxB7p+G4p/i8p/c55XDTyhsVikaer5x0R/IGCLkdH/v39/bV27VrVqVPHZvqOHTt03333KS4uLq/qyzV+3QMAALjDpFyWjmyWlv9HuvjPlWleQVLHGVKZZpKbt13Lg+MiG6Agy9GR//T0dLm6umaa7urqqvT09FwXBQAAAAf272FpUff/C/6SdOmctKS3dPaQ3coCAEeWo/DfokUL/fe//9Xx48et0/7++28NHTpULVu2zLPiAAAA4GBSk6Rts7I+xd8Y6cc3peRLt78uAHBwOQr/M2bMUEJCgsLDwxUREaGIiAiVLl1aCQkJmj59el7XCAAAAEeRfFE6+ev120//LqVcvH31AEABkaM7b5QoUUI///yz1q5dqwMHDkiSIiMj1apVqzwtDgAAAA7GzUsKqSAd/znr9uDykovX7a0JAAqAWzry//3336tSpUpKSEiQxWLRvffeq8GDB2vw4MGqU6eOKleurE2bNuVXrQAAALjbuXhI9QdJFkvW7Y2fldy54R8A5LVbCv9vvfWW+vfvn+WdMf39/TVgwABNnTo1z4oDAACAAwoqLXVfIHn4/980d1+pyxypUFn71QUADuyWHvVXqlQprVq1SpGRkVm2HzhwQPfdd5+OHj2aZwXmFo/zAAAAuAOlpkgXTkoXTksykk8RybeI5Oxm78rgwMgGKMhu6Zr/U6dOZfmIP2tnLi76559/rtsOAAAASJJcXKWAEldeAIB8d0un/RcrVky//fbbddv37t2rokWL5rooAAAAAACQd24p/Ldr106jR49WYmJiprbLly/r5ZdfVvv27fOsOAAAAAAAkHu3dM3/qVOnVKtWLTk7O2vQoEGqUKGCpCvX+r/zzjtKS0vTzz//rCJFiuRbwbeK63oAAAAASGQDFGy3dM1/kSJFtGXLFj311FMaNWqUMn43sFgsat26td555507KvgDAAAAAIBbDP/SlTv+f/PNN/r333918OBBGWNUrlw5BQYG5kd9AAAAAAAgl245/GcIDAxUnTp18rIWAAAAAACQD27phn8AAAAAAODuQ/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHR/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHZ9fwP2vWLFWrVk1+fn7y8/NT/fr19e2331rbExMTNXDgQAUHB8vHx0ddu3bVqVOn7FgxAAAAAAB3H7uG/+LFi2vSpEnatWuXfvrpJ7Vo0UKdOnXS77//LkkaOnSovvzySy1dulQ//PCDjh8/ri5dutizZAAAAAAA7joWY4yxdxFXCwoK0muvvaYHH3xQISEhWrRokR588EFJ0oEDBxQZGamtW7fqnnvuyVZ/CQkJ8vf3V3x8vPz8/PKzdAAAAAB3MLIBCrI75pr/tLQ0LV68WBcvXlT9+vW1a9cupaSkqFWrVtZ5KlasqJIlS2rr1q3X7ScpKUkJCQk2LwAAAAAACjK7h/9ff/1VPj4+cnd313/+8x8tX75clSpV0smTJ+Xm5qaAgACb+YsUKaKTJ09et7+JEyfK39/f+ipRokQ+rwEAAAAAAHc2u4f/ChUqaPfu3dq+fbueeuop9e3bV/v27ctxf6NGjVJ8fLz1dezYsTysFgAAAACAu4+LvQtwc3NT2bJlJUlRUVHauXOnpk2bph49eig5OVlxcXE2R/9PnTql0NDQ6/bn7u4ud3f3/C4bAAAAAIC7ht2P/F8rPT1dSUlJioqKkqurq9atW2dti46O1tGjR1W/fn07VggAAAAAwN3Frkf+R40apbZt26pkyZI6f/68Fi1apA0bNmj16tXy9/fX448/rmHDhikoKEh+fn4aPHiw6tevn+07/QMAAAAAADuH/9OnT6tPnz46ceKE/P39Va1aNa1evVr33nuvJOnNN9+Uk5OTunbtqqSkJLVu3VozZ860Z8kAAAAAANx1LMYYY+8i8hPP8gQAAAAgkQ1QsN1x1/wDAAAAAIC8RfgHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHR/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHR/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHR/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHR/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHR/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHR/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMHZNfxPnDhRderUka+vrwoXLqzOnTsrOjraZp7ExEQNHDhQwcHB8vHxUdeuXXXq1Ck7VQwAAAAAwN3HruH/hx9+0MCBA7Vt2zZ99913SklJ0X333aeLFy9a5xk6dKi+/PJLLV26VD/88IOOHz+uLl262LFqAAAAAADuLhZjjLF3ERn++ecfFS5cWD/88IOaNGmi+Ph4hYSEaNGiRXrwwQclSQcOHFBkZKS2bt2qe+6556Z9JiQkyN/fX/Hx8fLz88vvVQAAAABwhyIboCC7o675j4+PlyQFBQVJknbt2qWUlBS1atXKOk/FihVVsmRJbd26Ncs+kpKSlJCQYPMCAAAAAKAgu2PCf3p6up555hk1bNhQVapUkSSdPHlSbm5uCggIsJm3SJEiOnnyZJb9TJw4Uf7+/tZXiRIl8rt0AAAAAADuaHdM+B84cKB+++03LV68OFf9jBo1SvHx8dbXsWPH8qhCAAAAAADuTi72LkCSBg0apK+++kobN25U8eLFrdNDQ0OVnJysuLg4m6P/p06dUmhoaJZ9ubu7y93dPb9LBgAAAADgrmHXI//GGA0aNEjLly/X999/r9KlS9u0R0VFydXVVevWrbNOi46O1tGjR1W/fv3bXS4AAAAAAHclux75HzhwoBYtWqQvvvhCvr6+1uv4/f395enpKX9/fz3++OMaNmyYgoKC5Ofnp8GDB6t+/frZutM/AAAAAACw86P+LBZLltPnzp2rfv36SZISExP17LPP6pNPPlFSUpJat26tmTNnXve0/2vxOA8AAAAAEtkABZtdw//twBccAAAAgEQ2QMF2x9ztHwAAAAAA5A/CPwAAAAAADo7wDwAAAACAgyP8AwAAAADg4Aj/AAAAAAA4OMI/AAAAAAAOjvAPAAAAAICDI/wDAAAAAODgCP8AAAAAADg4wj8AAAAAAA6O8A8AAAAAgIMj/AMAAAAA4OAI/wAAAAAAODjCPwAAAAAADo7wDwAAAACAgyP8AwAAAADg4Aj/AAAAAAA4OMI/AAAAAAAOjvAPAAAAAICDI/wDAAAAAODgCP8AAAAAADg4wj8AAAAAAA6O8A8AAAAAgIMj/AMAAAAA4OAI/wAAAAAAODjCPwAAAAAADo7wDwAAAACAgyP8AwAAAADg4Aj/AAAAAAA4OMI/AAAAAAAOjvAPAAAAAICDI/wDAAAAAODgCP8AAAAAADg4wj8AAAAAAA6O8A8AAAAAgIMj/AMAAAAA4OAI/wAAAAAAODjCPwAAAAAADs7F3gUAN5N4IUUX45N0/GCcXN2dVTTCX15+7nJ1d7Z3aQAAAABwVyD84452KSFJmz6N0cGfTlunWZwsavFIRZWpGSI3DzZhAAAAALgZTvvHHe3P3Wdsgr8kmXSjdfP368K5JDtVBQAAAAB3F8I/7lgX45P0y5oj120/sO3EbawGAAAAAO5ehH/csUy60eXzKddtTzhzWSbd3MaKAAAAAODuRPjHHcvV3VlFyvhftz28WiFZnCy3sSIAAAAAuDsR/m+TtIsXlfzXX0o+elSp//5r73LuCu5erqrfuYwsWeR7L383FSsfePuLAgAAAIC7EOH/Nkg+elQnRo3Softa69B9rXXsiSd0ec8epSdxw7qbCSzqrU7DaiqoqPeVCRYpvGqwHni2lnyDPOxbHAAAAADcJSzGGIe+aDohIUH+/v6Kj4+Xn5/fbV9+yokTOtyzl1JPnbJtcHFR6c+WyqNixdte093oUkKyki+nyuJkkYePq9w9ecQfAAAAbo29swFgTxz5z2cXt2/PHPwlKTVVp6e9rbQLF25/UXchLz83BRTxkn+IJ8EfAAAAAG4R4T8fmbQ0XVi77rrtl3/6SekXLt7GigAAAAAABRHhPx9ZnJ3lEhp63XbnwEBZXJxvY0UAAAAAgIKI8J/PAro9eN224Mcek0uhQrexGgAAAABAQUT4z2euYWEq8vJLuvZ5dT733iufli3sVBUAAAAAoCDhzmn5zNnXV/4dO8q7fgNd2r5N6Rcvyrt+fbkUKSKXoCB7lwcAAAAAKAAI/7eBs7e3nL295R5eyt6lAAAAAAAKIE77BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfAAAAAAAHR/gHAAAAAMDBEf4BAAAAAHBwhH8AAAAAABwc4R8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcnIu9C8hvxhhJUkJCgp0rAQAAAGBPGZkgIyMABYnDh//z589LkkqUKGHnSgAAAADcCc6fPy9/f397lwHcVhbj4D97paen6/jx4/L19ZXFYrF3ObmWkJCgEiVK6NixY/Lz87N3OQ6Dcc0fjGv+YFzzB+OaPxjX/MPY5g/GNX/cKeNqjNH58+cVFhYmJyeugEbB4vBH/p2cnFS8eHF7l5Hn/Pz8+A9SPmBc8wfjmj8Y1/zBuOYPxjX/MLb5g3HNH3fCuHLEHwUVP3cBAAAAAODgCP8AAAAAADg4wv9dxt3dXS+//LLc3d3tXYpDYVzzB+OaPxjX/MG45g/GNf8wtvmDcc0fjCtgfw5/wz8AAAAAAAo6jvwDAAAAAODgCP8AAAAAADg4wj8AAAAAAA6O8A8AAAAAgIMj/N9m77zzjsLDw+Xh4aF69eppx44d15139uzZaty4sQIDAxUYGKhWrVplmt9isWT5eu2116zzhIeHZ2qfNGlSvq2jPdzKuC5btky1a9dWQECAvL29VaNGDS1YsMBmHmOMXnrpJRUtWlSenp5q1aqVYmJibOY5d+6cevfuLT8/PwUEBOjxxx/XhQsX8mX97CUvxzUlJUUjR45U1apV5e3trbCwMPXp00fHjx+36Yft1VZ2ttd+/fplGrM2bdrYzFMQtlcp78eWfewVtzKuV1u8eLEsFos6d+5sM5197BV5Oa7sY/9PXm+v7GOvyOtxZf8K2IHBbbN48WLj5uZmPvzwQ/P777+b/v37m4CAAHPq1Kks53/ooYfMO++8Y3755Rezf/9+069fP+Pv72/++usv6zwnTpyweX344YfGYrGYQ4cOWecpVaqUGTdunM18Fy5cyPf1vV1udVzXr19vli1bZvbt22cOHjxo3nrrLePs7GxWrVplnWfSpEnG39/frFixwuzZs8d07NjRlC5d2ly+fNk6T5s2bUz16tXNtm3bzKZNm0zZsmVNr1698n19b5e8Hte4uDjTqlUrs2TJEnPgwAGzdetWU7duXRMVFWXTD9urrexsr3379jVt2rSxGbNz587Z9OPo26sx+TO27GNvfVwzxMbGmmLFipnGjRubTp062bSxj837cWUfe0V+bK/sY/NnXNm/Arcf4f82qlu3rhk4cKD1fVpamgkLCzMTJ07M1udTU1ONr6+vmT9//nXn6dSpk2nRooXNtFKlSpk333wzRzXfDXI7rsYYU7NmTfPiiy8aY4xJT083oaGh5rXXXrO2x8XFGXd3d/PJJ58YY4zZt2+fkWR27txpnefbb781FovF/P3337ldpTtCXo9rVnbs2GEkmSNHjlinsb3e3LXj2rdv30z/qLpaQdhejbk92yz72OyNa2pqqmnQoIGZM2dOpu2TfewVeT2uWWEfmzfjyj729myvBXH/CtxunPZ/myQnJ2vXrl1q1aqVdZqTk5NatWqlrVu3ZquPS5cuKSUlRUFBQVm2nzp1Sl9//bUef/zxTG2TJk1ScHCwatasqddee02pqak5W5E7TG7H1RijdevWKTo6Wk2aNJEkxcbG6uTJkzZ9+vv7q169etY+t27dqoCAANWuXds6T6tWreTk5KTt27fn1erZTX6Ma1bi4+NlsVgUEBBgM53tNWs3GtcNGzaocOHCqlChgp566imdPXvW2ubo26t0e7ZZ9rFXZGdcx40bp8KFC2c5Vuxj82dcs8I+Nu/GtSDvY2/H9loQ96+APbjYu4CC4syZM0pLS1ORIkVsphcpUkQHDhzIVh8jR45UWFiYzc73avPnz5evr6+6dOliM33IkCGqVauWgoKCtGXLFo0aNUonTpzQ1KlTc7Yyd5Ccjmt8fLyKFSumpKQkOTs7a+bMmbr33nslSSdPnrT2cW2fGW0nT55U4cKFbdpdXFwUFBRknedulh/jeq3ExESNHDlSvXr1kp+fn3U622tmNxvXNm3aqEuXLipdurQOHTqk559/Xm3bttXWrVvl7Ozs8NurdHu2Wfax/+dG4/rjjz/qgw8+0O7du7NsZx+bP+N6Lfax/ye341rQ97G3Y3stiPtXwB4I/3eJSZMmafHixdqwYYM8PDyynOfDDz9U7969M7UPGzbM+v+rVasmNzc3DRgwQBMnTpS7u3u+1n2n8vX11e7du3XhwgWtW7dOw4YNU5kyZdSsWTN7l3ZXy+64pqSkqHv37jLGaNasWTZtbK+Z3Wxce/bsaZ23atWqqlatmiIiIrRhwwa1bNnSTlXfHW5lX8A+9ubOnz+vRx55RLNnz1ahQoXsXY7DuNVxZR+bPdkdV/axtyYn+wH2r8DtQfi/TQoVKiRnZ2edOnXKZvqpU6cUGhp6w8++/vrrmjRpktauXatq1aplOc+mTZsUHR2tJUuW3LSWevXqKTU1VYcPH1aFChWyvxJ3oJyOq5OTk8qWLStJqlGjhvbv36+JEyeqWbNm1s+dOnVKRYsWtemzRo0akqTQ0FCdPn3aps/U1FSdO3fupn/Pu0F+jGuGjH+UHjlyRN9//73NEamssL1mb1yvVqZMGRUqVEgHDx5Uy5YtHX57lfJ/bNnHZm9cDx06pMOHD6tDhw7Waenp6ZKuHAmNjo5mH6v8GdeIiAhJ7GPza1yvVtD2sfk9rgV1/wrYA9f83yZubm6KiorSunXrrNPS09O1bt061a9f/7qfmzJlisaPH69Vq1bZXEt2rQ8++EBRUVGqXr36TWvZvXu3nJycMp2idjfK6bheKz09XUlJSZKk0qVLKzQ01KbPhIQEbd++3dpn/fr1FRcXp127dlnn+f7775Wenq569erldrXsLj/GVfq/f5TGxMRo7dq1Cg4OvmkfbK+ZXTuu1/rrr7909uxZa7By9O1Vyv+xZR+bvXGtWLGifv31V+3evdv66tixo5o3b67du3erRIkS7GOVP+MqsY/Nr3G9VkHbx+b3uBbU/StgF/a822BBs3jxYuPu7m7mzZtn9u3bZ5588kkTEBBgTp48aYwx5pFHHjH/+9//rPNPmjTJuLm5mc8++8zmESfnz5+36Tc+Pt54eXmZWbNmZVrmli1bzJtvvml2795tDh06ZD7++GMTEhJi+vTpk78rexvd6rhOmDDBrFmzxhw6dMjs27fPvP7668bFxcXMnj3bOs+kSZNMQECA+eKLL8zevXtNp06dsnwMVc2aNc327dvNjz/+aMqVK+dwj/XJy3FNTk42HTt2NMWLFze7d++22aaTkpKMMWyvxtz6uJ4/f94MHz7cbN261cTGxpq1a9eaWrVqmXLlypnExERrP46+vRqTP/sCY9jH3uq4Xiuru3yzj837cWUfe0Vejyv72CvyYz9gDPtX4HYj/N9m06dPNyVLljRubm6mbt26Ztu2bda2pk2bmr59+1rflypVykjK9Hr55Zdt+nzvvfeMp6eniYuLy7S8Xbt2mXr16hl/f3/j4eFhIiMjzYQJE2z+g+UIbmVcX3jhBVO2bFnj4eFhAgMDTf369c3ixYtt+ktPTzejR482RYoUMe7u7qZly5YmOjraZp6zZ8+aXr16GR8fH+Pn52ceffTRTD/M3O3yclxjY2Oz3J4lmfXr1xtj2F6NufVxvXTpkrnvvvtMSEiIcXV1NaVKlTL9+/e3/oMsQ0HYXo3J+32BMexjjbm1cb1WVv/oZx97RV6OK/vY/5OX48o+9v/k9X7AGPavwO1mMcaY2366AQAAAAAAuG245h8AAAAAAAdH+AcAAAAAwMER/gEAAAAAcHCEfwAAAAAAHBzhHwAAAAAAB0f4BwAAAADAwRH+AQAAAABwcIR/AAAAAAAcHOEfQIFjsVi0YsUK6/sDBw7onnvukYeHh2rUqHHdaY5k3rx5CggIsHcZDuPabSqvnD17VoULF9bhw4fzvO+8sG/fPhUvXlwXL160dykAAOAmCP8AHEK/fv1ksVhksVjk6uqqIkWK6N5779WHH36o9PR0m3lPnDihtm3bWt+//PLL8vb2VnR0tNatW3fdaY6kR48e+uOPP+xdBm7i1VdfVadOnRQeHi5JOnz4sHU7v/a1bds26+eSk5M1ZcoUVa9eXV5eXipUqJAaNmyouXPnKiUlRZI0ZsyYTH1UrFjRZvmJiYkaOHCggoOD5ePjo65du+rUqVPW9kqVKumee+7R1KlT838wAABArhD+ATiMNm3a6MSJEzp8+LC+/fZbNW/eXP/973/Vvn17paamWucLDQ2Vu7u79f2hQ4fUqFEjlSpVSsHBwdeddquSk5Nzt0L5yNPTU4ULF7Z3GTmSEV4d3aVLl/TBBx/o8ccfz9S2du1anThxwuYVFRUl6cp217p1a02aNElPPvmktmzZoh07dmjgwIGaPn26fv/9d2s/lStXtunjxx9/tFnO0KFD9eWXX2rp0qX64YcfdPz4cXXp0sVmnkcffVSzZs2y+Y7ZkzHmjqkFAIA7igEAB9C3b1/TqVOnTNPXrVtnJJnZs2dbp0kyy5cvt/7/q18vv/xyltOMMebo0aOmW7duxt/f3wQGBpqOHTua2NjYTDW88sorpmjRoiY8PPyWPvfaa6+Z0NBQExQUZJ5++mmTnJxsnScxMdGMGDHCFC9e3Li5uZmIiAgzZ84ca/uvv/5q2rRpY7y9vU3hwoXNww8/bP7555/rjtfcuXONv7+/9f3LL79sqlevbj766CNTqlQp4+fnZ3r06GESEhKu28eZM2dMz549TVhYmPH09DRVqlQxixYtsra/9957pmjRoiYtLc3mcx07djSPPvqo9f2KFStMzZo1jbu7uyldurQZM2aMSUlJsfl7zZw503To0MF4eXmZl19+2aSmpprHHnvMhIeHGw8PD1O+fHnz1ltv2SwnJSXFDB482Pj7+5ugoCAzYsQI06dPH5vtJC0tzUyYMMHaT7Vq1czSpUuvu87Xc/U2ZYwxe/fuNc2bNzceHh4mKCjI9O/f35w/f/6Walu6dKkJCQmxWU5sbKyRZH755Zfr1jJ58mTj5ORkfv7550xtycnJ5sKFC8aY//ubX09cXJxxdXW1GY/9+/cbSWbr1q3WaUlJScbd3d2sXbv2un3t3r3bNGvWzPj4+BhfX19Tq1Yts3PnTmv7jz/+aJo2bWo8PT1NQECAue+++8y5c+eMMVe2/cGDB5uQkBDj7u5uGjZsaHbs2GH97Pr1640k880335hatWoZV1dXs379+pv+bc+dO2ceeughU6hQIePh4WHKli1rPvzww+uuAwAAdzuO/ANwaC1atFD16tW1bNmyLNtPnDihypUr69lnn9WJEyc0fPjwLKelpKSodevW8vX11aZNm7R582b5+PioTZs2Nkf4161bp+joaH333Xf66quvsv259evX69ChQ1q/fr3mz5+vefPmad68edb2Pn366JNPPtHbb7+t/fv367333pOPj48kKS4uTi1atFDNmjX1008/adWqVTp16pS6d+9+S2N16NAhrVixQl999ZW++uor/fDDD5o0adJ1509MTFRUVJS+/vpr/fbbb3ryySf1yCOPaMeOHZKkbt266ezZs1q/fr31M+fO/b/27j0oyur/A/gbVi67DCkORKACKYKQSICASOVMEFtjDAJDsKxC4oUE5WIgyihJTFyiwi5K+IeKtEnrNKMzIjcnNAJrNjDism6wCCgz0ARmIrAgnN8fjk88siCk35/KfF4zzHDOec5zPs/ln/Ocy/ajrKwMUqkUAFBdXY2IiAjEx8ejpaUFBQUFOHHiBD766CNeWwcPHkRgYCAaGxsRFRWF8fFxLF68GKdPn0ZLSwvS0tKQmpoKuVzO1cnJyYFMJsPx48dRU1ODf/75Z9K6/KysLJw8eRJff/01mpubkZiYiI0bN+LSpUuzuncT3blzB2KxGCYmJlAoFDh9+jQuXLiAnTt3ziq26upqbjR/NmQyGXx9feHi4jKpTE9PD0ZGRly6tbUVlpaWWLp0KaRSKbq6uriyuro6jI6OwtfXl8tbsWIFrKyscPnyZS5PX18fL7/8Mqqrq6eMSSqVYvHixVAoFKirq8PevXuhp6cHAPjtt9/g4+MDR0dHXL58GT/99BP8/f0xNjYGANizZw++//57FBYWor6+Hra2thCLxejv7+e1sXfvXmRnZ0OpVGLVqlUPfbYHDhxAS0sLSktLoVQqkZ+fD1NT09ncakIIIeTZ8qS/PhBCyOMw1cg/Y4yFhoYyBwcHLo0HRmmdnZ250f2p8oqKipi9vT0bHx/n8jQaDRMKhay8vJyLwdzcnGk0mlnXs7a2Znfv3uWOCQkJYaGhoYwxxlQqFQPAKisrtV5fRkYG8/Pz4+Vdv36dAWAqlUprHW0j/yKRiDfSn5yczDw9PbXWn8r69evZ+++/z6UDAgJYVFQUly4oKGCWlpbcbAAfHx+WmZnJO0dRURGzsLDg0gBYQkLCQ9uOjY1lwcHBXNrc3Jzl5uZy6bt37zIrKyvuPRkeHmYikYjV1tbyzrNlyxYmkUhmcLX/mvhOHT16lJmYmHAj7IwxVlJSwnR1dVlPT8+MYmNs8r1j7N+Rf6FQyIyMjHh/9wmFQhYXF/fQmM+fP8/kcjlraGhgZWVlzMvLi1lZWXHvgEwmY/r6+pPqubu7sz179vDyAgMD2bvvvjtlW8bGxuzEiRNayyQSCfP29tZaNjAwwPT09JhMJuPyRkZGmKWlJfv4448ZY/+O/J85c4Y7ZibP1t/fnzcDhRBCCJnr5j3B7w6EEPL/gjEGHR2dRzpHQ0MD2traYGxszMsfHh6GWq3m0k5OTtDX1591vZdeegkCgYBLW1hYoLGxEcC9kVGBQIB169ZNGVtVVRU3E2AitVoNOzu7GV2jjY0NL04LCwv8+eefUx4/NjaGzMxMyOVydHd3Y2RkBBqNBiKRiDtGKpVi27ZtOHLkCAwMDCCTyRAWFgZdXV0u9pqaGt5I/9jYGIaHhzE4OMida/Xq1ZPaP3z4MI4dO4auri4MDQ1hZGSE+2WGW7duobe3Fx4eHtzxAoEAbm5u3AaQbW1tGBwcxBtvvME778jIiNZRcwDIzMxEZmYml25paYGVlRXvGKVSCWdnZ94Iu7e3N8bHx6FSqWBoaPjQ2ABgaGgIhoaGWuP47rvv4ODgoLWMMaY1/0ETN71ctWoVPD09YW1tDblcrnWfgekIhUIMDg5OWb57925s3boVRUVF8PX1RUhICJYtWwbg3vsdEhKitZ5arcbo6Ci8vb25PD09PXh4eECpVPKOnfiOzOTZ7tixA8HBwaivr4efnx82bNiAtWvXzuq6CSGEkGcJdf4JIXOeUqnEiy+++EjnGBgYgJubG2Qy2aQyMzMz7v+JHb7Z1Ls/Bfo+HR0driMoFAofGpu/vz9ycnImlVlYWExbd6LpYtAmNzcXn3/+OQ4dOgQnJycYGRkhISGBt5zB398fjDGUlJTA3d0d1dXVyMvL48Wenp4+aRM5ALyO74P3tbi4GElJSfj000/h5eUFY2Nj5Obm4pdffpnx9Q4MDAAASkpKsGjRIl7ZxA0hJ3rvvfd4yyksLS1n3N5smZqa4ubNm1rLlixZAltbW61ldnZ2uHr16qzbW7BgAezs7NDW1gbg3saYIyMj+Pvvv3k/C9nb24sXXniBV7e/v5/rzGtz8OBBhIeHo6SkBKWlpfjggw9QXFyMwMDAh77fMzXxHZnJs33rrbfQ2dmJ8+fPo7KyEj4+PoiNjcUnn3zyWOIhhBBCnja05p8QMqf98MMPaGxsRHBw8COdx9XVFa2trXj++edha2vL+5s/f/5jrzeRk5MTxsfHp1yH7urqiubmZtjY2Exq48FO8+NUU1ODgIAAbNy4Ec7Ozli6dOmknw80NDREUFAQZDIZTp06BXt7e7i6uvJiV6lUk+K2tbXlZgdM1fbatWsRExMDFxcX2Nra8mZSzJ8/H+bm5lAoFFze2NgY6uvrubSjoyMMDAzQ1dU1qe0lS5ZobXfhwoW84+bNm/wN3cHBAQ0NDbhz5w4vXl1dXdjb288oNgBwcXFBS0vLlPdgKuHh4bhw4QKuXLkyqWx0dJQX10QDAwNQq9XcByM3Nzfo6enxfupSpVKhq6sLXl5evLpNTU1Tzpa4z87ODomJiaioqEBQUBCOHz8O4N6sg6l+TnPZsmXQ19dHTU0N7xoUCgUcHR2nbGumz9bMzAyRkZH45ptvcOjQIRw9enTaayCEEEKeZdT5J4TMGRqNBj09Peju7kZ9fT0yMzMREBCAt99+GxEREY90bqlUClNTUwQEBKC6uhrXrl3DxYsXERcXhxs3bjz2ehPZ2NggMjISUVFROHPmDHeO+5vbxcbGor+/HxKJBAqFAmq1GuXl5di8eTO3adr/wvLly1FZWYna2loolUpER0fzfgP+PqlUipKSEhw7dozb6O++tLQ0nDx5Eunp6WhuboZSqURxcTH279//0LZ//fVXlJeX448//sCBAwd4nWkA2LVrF7KysnD27FmoVCrEx8fj5s2b3BIQY2NjJCUlITExEYWFhVCr1aivr8eXX36JwsLC/3xfpFIpDA0NERkZiaamJlRVVWHXrl3YtGkTzM3NZxQbAIjFYjQ3N2sd/e/r60NPTw/vb3h4GACQkJAAb29v+Pj44PDhw2hoaEB7ezvkcjnWrFmD1tZWAEBSUhIuXbqEjo4O1NbWIjAwEAKBABKJBMC9DyhbtmzB7t27UVVVhbq6OmzevBleXl5Ys2YNF0tHRwe6u7t5GwNONDQ0hJ07d+LixYvo7OxETU0NFAoFt2xh3759UCgUiImJwe+//46rV68iPz8ff/31F4yMjLBjxw4kJyejrKwMLS0t2LZtGwYHB6ddmjCTZ5uWloazZ8+ira0Nzc3NOHfu3JRLKQghhJC5gKb9E0LmjLKyMlhYWGDevHkwMTGBs7MzvvjiC0RGRk47ijwTIpEIP/74I1JSUhAUFITbt29j0aJF8PHxwXPPPffY6z0oPz8fqampiImJQV9fH6ysrJCamgrg3tTzmpoapKSkwM/PDxqNBtbW1njzzTcf+bqns3//frS3t0MsFkMkEmH79u3YsGEDbt26xTvu9ddfx8KFC6FSqRAeHs4rE4vFOHfuHD788EPk5ORAT08PK1aswNatW6dtOzo6GleuXEFoaCh0dHQgkUgQExOD0tJS7piUlBT09PQgIiICAoEA27dvh1gs5u2tkJGRATMzM2RlZaG9vR0LFiyAq6srd2//C5FIhPLycsTHx8Pd3R0ikQjBwcH47LPPZhWbk5MTXF1dIZfLER0dzWtDW0f71KlTCAsLg4GBASorK5GXl4eCggIkJSVBJBLBwcEBcXFxWLlyJQDgxo0bkEgk6Ovrg5mZGV555RX8/PPPvOUoeXl50NXVRXBwMDQaDcRiMY4cOTKpXT8/P1hbW2u9HwKBAH19fYiIiEBvby9MTU0RFBSE9PR0APdmBFRUVCA1NRUeHh4QCoXw9PTkPkJkZ2djfHwcmzZtwu3bt7F69WqUl5fDxMRk2ufwsGerr6+Pffv2oaOjA0KhEK+++iqKi4unPSchhBDyLNNhM90ZiBBCCHmGjY+Pw8HBAe+88w4yMjKedDg8U8VWUlKC5ORkNDU1/U8/5PxXIyMjWL58Ob799lvepnyEEEIIefrQyD8hhJA5qbOzExUVFVi3bh00Gg2++uorXLt2bdLsg6c5tvXr16O1tRXd3d1T7kPwJHV1dSE1NZU6/oQQQsgzgEb+CSGEzEnXr19HWFgYmpqawBjDypUrkZ2djddee+1Jh/ZUx0YIIYSQuYk6/4QQQgghhBBCyBz39C0gJIQQQgghhBBCyGNFnX9CCCGEEEIIIWSOo84/IYQQQgghhBAyx1HnnxBCCCGEEEIImeOo808IIYQQQgghhMxx1PknhBBCCCGEEELmOOr8E0IIIYQQQgghcxx1/gkhhBBCCCGEkDnu/wByJt5n9boOtgAAAABJRU5ErkJggg==", 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